Intelligent Structure Research Articles

Deep learning-based intelligent control of moisture at the exit of blade charging process in cigarette production

Abstract Currently, in the production of cigarettes in the blade, charging export moisture control means is relatively single and can not effectively guarantee the excellent quality of cigarette filament. In this paper, first of all, the working principle of the tobacco blade charging machine is introduced, and the moisture of the tobacco leaf for the charging machine is dynamically analyzed, and the influence of the return air temperature control of the charging machine on the export moisture of the blade charging process is explored. Secondly, based on the traditional PID controller, an adaptive fuzzy PID controller is established by combining adaptive fuzzy rules, and then the stacked noise-reducing self-encoder in deep learning is combined with the adaptive fuzzy PID control to design the intelligent control structure of export moisture of leaf charging process. Finally, the effectiveness of export moisture intelligence control, process capability index, and the effect before and after application were analyzed in controlled experiments, respectively. The results show that the difference between the predicted value and the real value of blade export moisture in this paper’s method is only 0.5%, and the process capability index of this paper’s method is improved by 1.48 compared with the PID controller, and it can control the temperature of the return air of the charging machine in the range of 56.86℃~57.21℃. The intelligent control method of export moisture introduced by deep learning can accurately control the export moisture of the leaf dosing process, which effectively ensures the quality of tobacco filament making.

An Intelligent IoT Framework for Heart Diseases Prediction Using Harris Hawk Optimized GRNN

Recently, Heart diseases is considered as the one of deadliest diseases which has resulted in the increased death rates across the globe. Predicting heart diseases requires vast experiences along with advanced knowledge. IoT and AI are two emerging technologies that help in heart disease prediction. High diagnostic accuracy with minimal processing overhead, however, continues to be a design problem for researchers. To address this problem, this paper develops the Intelligent IoT structure for the better prediction of cardiac diseases employing Harris Hawk Optimized Gated Modified Recurrent Units (HHO-M-GRU). The paper also proposes the real time data collection using IoT wearable test beds which comprises of electrocardiography sensors (ECG) interfaced with MICOTT Boards ESP8266 transceivers. For later processing, the acquired data are saved on the cloud. The proposed deep learning network is utilized for evaluating the received heart data and used for predicting the heart diseases. Additionally, the suggested HHO-GRU is trained with the versatile datasets which consist of normal and abnormal stages of heart diseases. By calculating the suggested model's performance measures, including accuracy, precision, recall, specificity, and F1-score, a thorough experiment is conducted. The proposed framework was implemented in Keras libraries with Tensorflow 2.1.1 as backend. Furthermore, prediction performance and complexity overhead is compared using the other cutting-edge deep learning algorithms already in use to demonstrate the model's superiority. in predicting the heart diseases. The suggested approach beats previous models for learning with respect to of accurate prediction (99%) and minimal computing overhead, according to the results.

Prediction of ternary alkaline-earth metal Sn(II) and Pb(II) chlorides with potential applications as p-type transparent conductors.

Non-metallic Sn(II) and Pb(II) compounds, particularly those with p-type properties, are essential functional materials due to their notable electronic arrangement and chemical characteristics. The presence of additional Sn(II) and Pb(II) chlorides is suggested by the existence of known Sn(II) and Pb(II) compounds. By utilizing first-principles calculations and swarm intelligence structure search techniques, we have predicted the existence of up to seven new ternary alkaline-earth metal chlorides: ABCl4 (where A = Sr and B = Sn or Pb) and AB2Cl6 (where A = Mg, Ca, or Ba and B = Sn, or A = Ca or Sr and B = Pb). These seven chlorides are in the divalent state. The interaction between Sn-5s (or Pb-6s) and Cl-3p in these compounds creates an anti-bonding effect in the upper valence bands, which enhances defect tolerance and promotes high p-type conductivity. These stable chlorides exhibit notable electronic properties, including wide band gaps ranging from 3.91 to 4.94 eV, broad hole effective masses ranging from 0.93 to 5.62 m0, and high valence band alignments ranging from 6.83 to 8.38 eV under vacuum. In particular, Ca/BaSn2Cl6 and CaPb2Cl6 have the potential to be used as p-type transparent conductors due to their favorable properties, including a lower hole effective mass (0.93 m0 for CaPb2Cl6) and higher ionization potentials (6.83/7.05 eV for Ba/CaSn2Cl6). Furthermore, the predicted CaPb2Cl6 crystal exhibits attenuated negative linear compressibility and negative zero-linear-compressibility along the c-axis in different pressure ranges due to its wine-rack structure. This report highlights potential applications for alkaline-earth metal Sn(II) and Pb(II) chlorides, including their use as transparent conductors, particularly p-type conductors.

Exploration of Live Teaching（Ⅲ）—Intelligence Structure of Director

Exploration of Live Teaching（Ⅲ）—Intelligence Structure of Director

INTELLIGENCE AND PSYCHOLOGICAL KNOWLEDGE IN POST-NON-CLASSICAL SCIENTIFIC RATIONALITY (REVIEW AND ANALYTICAL NOTES)

Relevance. Intelligence as a complex phenomenon and the mental property of a person is studied in various interpretations, in the social and humanitarian discourse. The works of philosophers, political scientists, sociologists, cult urologists, psychologists, teachers and representatives of other sciences are devoted to the issues of the essence, structure, functional and activity characteristics of intelligence. The interdict capillarity and systemic complexity of the phenomenon of intelligence raises the question of the relationship and conjugation of this concept with the current level of development of psychological knowledge within the framework of modern scientific rationality. The disclosure of the psychological properties of intelligence makes it possible to objectively cognize its social and subjective nature.
 Materials and methods. The correlation and integration of knowledge about intelligence as a subjective quality and the quality of a person is manifested in the conjugation of modern psychological scientific rationality. The study of psychological knowledge about intelligence is carried out through the consistent and complex application of axiological, activity, systemic, subjective approaches and modern concepts in the philosophy of science. To consider and build an objective picture of psychological knowledge about intelligence in modern scientific rationality, a systematic and analytical method, synthetic and analog studies, a deductive method and a method of conceptualization, abstraction and generalization are applied.
 Results. Knowledge of the psychological characteristics and features of the functioning of intelligence as a dynamic, emotional, intellectual and value phenomenon from the standpoint of post-non-classical scientific rationality allows us to consider it as a complex self-developing system inherent in a certain social subject and being a product of the development of culture.
 Discussions and Conclusions. The modern development of psychological knowledge indicates that today psychology considers various phenomena and complex psychosocial systems. As for the phenomenon of intelligence, its characteristics, from the standpoint of post-non-classical scientific rationality, are intellectuality, dynamism, communicativeness, subjectivity, activity and value-semantic properties and qualities that interact and are realized in a complex social environment, functioning in their systemic totality.

Power Bill Optimization for Grid-tie Solar PV-Battery House

This study focuses on the optimization of power bills for a house equipped with a grid-tie solar PV-battery system. Rather than adhering to conventional load scheduling practices or minimizing grid power usage at each time interval, a novel approach is adopted wherein the optimization is performed for the entire 24-hour period simultaneously. By directly incorporating time-of-use rates into the cost function, an absolute optimal solution is attained. The findings indicate that compared to single time step optimization, the proposed method results in a reduction of the power bill ranging from 6% to 10%, depending on load-generation variations. Furthermore, if the utility or government enforces the summer tariff consistently throughout the year, the savings escalate to a range of 15% to 22%. Introducing a more intelligent tariff structure can thus serve as an effective means to expedite the transition towards renewable energy by incentivizing individual investments in solar PV, battery systems, and smart home energy management.

Retracted: Design of Remote Monitoring Iot System Based on 5G and New Optical Fiber Intelligent Structure

Retracted: Design of Remote Monitoring Iot System Based on 5G and New Optical Fiber Intelligent Structure

SYSTEMIC NATURE OF INTELLIGENCE FORMATION

Abstract: it is shown that intelligence plays an important role in ensuring the normal functioning and development of society, in the implementation of scientific, technical and social progress, in the establishment of favorable interpersonal relationships, and in the formation and observance of the spiritual and cultural foundations of social life. The meaning and structure of the phenomenon of intelligence have been clarified. In connection with the significant complexity and contradiction of this phenomenon, the need for a systematic approach to the formation and development of intelligence is substantiated. It is proven that the systematicity helps to determine the possibilities, conditions and limitations characteristic of the processes of formation and development of intelligence. The structure of intelligence is summarized, which covers the high professionalism of an intellectual, his morality, decency and responsibility, value perception of his profession and humanistic system of life goals and values.

Особенности мотивации обучающихся с различным уровнем интеллектуальных способностей

Individual motivational factors depend on the intellectual potential. The research objective was to identify the characteristics of motivating students with different levels of intellectual abilities based on the motivational and intellectual spheres of their personality. The study was conducted at the Siberian Polytechnic College and Algorithmics Programming School, Kemerovo. It involved 85 students aged 15–19 y.o. R. Amthauer’s Intelligence Structure Test made it possible to determine their dominant motivations and psychometric intelligence indicators. The data were processed in line with the Fisher’s multifunctional φ-angular criterion. Students who stated self-development and professional achievements as their dominant motivations differed in cognitive abilities. The analysis can be of practical importance for teachers and psychologists. It provides a better understanding of how motivational factors can affect academic process and may help to customize approaches to teaching based on a more effective interaction between teachers and students.

High-Tg shape memory polyimide composites with “spot-plane” directional thermally conduction structure based on AlN nanoparticles and acidified graphene

In order to enhance the thermal conduction property and shorten response time of shape memory polyimide (SMPI), “spot-plane” directional thermally conductive acidified graphene/AlN/SMPI composites were prepared, in which AlN nanoparticles were used as “thermal conduction spots” and acidified graphene was used as two-dimensional “thermal conduction planes”. The SMPI composite doped with 6 wt% AlN and 0.8 wt% acidified graphene had good heat conducting properties (the in-plane thermal conductivity of 5.90 W m−1 K−1), excellent mechanical propertied (the tensile strength of 134.5 MPa, and Young’s modulus of 3.8 GPa), high shape memory transition temperature (at 419 ℃), and great thermal stability and shape memory properties. Compared with SMPI resin matrix, the response time of the acidified graphene/AlN/SMPI composite was shortened by 140 s, only 20 s, and it can be applied in flexible electronics, intelligent response structures and active deformation structures in high-temperature environment.

From Artificial Intelligence and Bayesian Statistics to Neuroanatomy: Connections, Analogies, and Applications

This study examines the interaction between artificial intelligence (AI), Bayesian statistics, and various key brain structures, such as the hippocampus, amygdala, and thalamic nuclei. The goal is to explore how Bayesian inference can contribute to the development of AI systems that simulate and optimize essential aspects of human cognition, such as decision-making, attention, learning, and memory. By analyzing the functions of these structures within the framework of Bayesian statistics, possible avenues are identified for improving the adaptability and efficiency of AI systems in problem-solving and decision-making. Additionally, the relevance of Brodmann areas in the context of AI and Bayesian statistics is considered. The knowledge gained from this study can provide a solid foundation for the design of increasingly advanced and human-centric AI systems, facilitating more effective and understandable interaction between humans and AI technologies.

AN APPLICATION OF A MATERIAL DEFECT DETECTION SYSTEM USING ARTIFICIAL INTELLIGENCE

In recent years, detecting defects in materials has become an important element in many industries, such as automotive, construction, textile manufacturing, and many others. Material defects, such as stains, cracks, dents, and others, can affect the quality and durability of the product, which can have serious consequences for safety, performance, and product quality. Therefore, it is important to detect and remove material defects as quickly and accurately as possible. Traditional methods of detecting material defects, such as manual inspections or simple visual algorithms, can be unreliable and, above all, time-consuming. In recent years, however, new tools based on intelligent algorithms (Liu et al., 2023) or structures (Neethu et al., 2015), such as neural networks, have emerged that enable automation and significantly increase the precision of defect detection. One of the most popular tools for object detection is the YOLOv5 (Redmon et al., 2016) (You Only Look Once version 5) model, which is based on neural networks and designed for fast and accurate object detection in images. The YOLOv5 model can be trained to detect specific types of material defects, such as stains, cracks, dents, etc. To use the YOLOv5 model for defect detection in materials, images of the material with specified objects of interest are needed, which will be used as input data for the model training. The YOLOv5 model analyses the images and determines rectangular frames around each defect indicating its location, and then assigns a selected label (in this case, the name of the defect). As a result, the YOLOv5 model is able to detect defects in new material images, regardless of their location, colour, and size. As the work progresses, it is also possible to prepare new images and retrain the model for new defects or to improve the performance of existing ones. As a result of the research, the obtained results at a 95% level of recognized defects certainly qualify the applied technology for professional use after appropriate workstation preparation and a sufficiently large amount of data (preferably 1000 or more).

Axial and radial reversibility and energy absorption behaviors of braided shape memory composite thin-walled tubes

Braided shape memory polymer composites (SMPCs), which integrate braided structures with shape memory polymers, are well-known for their superior mechanical properties, reversible deformation, and recovery capabilities. This study focused on exploring the reusable energy absorption capacity of intelligent braided SMPC tubes. The effects of braiding structure on compression modes, mechanical properties, energy absorption, shape memory performance, and repeatability under axial and radial compression have been comprehensively investigated. The results show that SMPC tubes have a shape recovery ratio exceeding 94 % after four cycles. As the number of compressions increased, both the mechanical and energy absorption properties decreased, particularly during the first compression. Higher energy absorption capacity has been observed in SMPC tubes with larger braiding angles, up to 32.51 J, 2.3 times that of pure TPU tubes. These findings offer potential approaches for the development of high-performance reusable protective devices and lightweight intelligent structures.

Is intelligence of preterm children different from full‐term children? Commentary on Rapuc et al. (2023)

Rapuc et al. (Journal of Child Psychology and Psychiatry, 2023) found that Full‐Scale IQ of preterm children is significantly lower than full‐term children. Also, although the structure of intelligence appeared to involve the same clusters of cognitive ability, relations between abilities were stronger in preterm children, implying that abilities are more differentiated in full‐term than in preterm children. This commentary examined if these findings hold when different modelling methods are used. Instead of the network analysis used in the target article (Rapuc et al., 2023), I used confirmatory factor analysis and special forms of structural equation modelling designed to capture interactions between processes and their differentiation from general cognitive ability. I found, in line with the target article, that premature children scored lower overall in cognitive ability, and that cognitive processes are more strongly related in preterm than in full‐term children. However, in contrast to the target article, specific abilities tended to differentiate with increasing general ability in preterm rather than in full‐term children. In full‐term children, increases in specific abilities were commensurate with increases in general ability. These differences may reflect slower development in preterm children and stronger dependence on executive processes dominating in preschool. Clinical implications are discussed.

Scale-Dependent MSG Formulations for Vibrational Study of Honeycomb Microplates Covered by Piezoelectric Nanocomposite Patches

This study investigates free vibration analysis of a microplate featuring a honeycomb (HC) core and incorporates two nanocomposite piezoelectric (NP) layers. Carbon nanotubes (CNTs) are hired to enhance the electro-mechanical performance of the piezoelectric patches, which are subjected to an externally applied electric voltage. All layers are tightly bonded together and are supported by an elastic substrate according to the Pasternak model, capable of withstanding both normal and shear loads. To establish the kinematic relations, a trigonometric plate theory is employed. The governing equations of motion are deduced through the application of Hamilton’s principle and variational technique. The modified strain gradient theory, which includes three material length-scale parameters (MLSPs), is used to account for the scale effect. An analytical approach based on Fourier series functions is employed to solve the differential equations of motion. Subsequently, the impact of various factors, such as the geometrical characteristics of the HC core, distribution patterns of CNTs, and other significant parameters, on the normalized frequencies of the model is assessed after validating the accuracy of the results. The outcomes of this research have the potential to facilitate the advancement and production of lightweight and intelligent structures and devices, ultimately enhancing their efficiency.

Neural tracking measures of speech intelligibility: Manipulating intelligibility while keeping acoustics unchanged.

Neural speech tracking has advanced our understanding of how our brains rapidly map an acoustic speech signal onto linguistic representations and ultimately meaning. It remains unclear, however, how speech intelligibility is related to the corresponding neural responses. Many studies addressing this question vary the level of intelligibility by manipulating the acoustic waveform, but this makes it difficult to cleanly disentangle the effects of intelligibility from underlying acoustical confounds. Here, using magnetoencephalography recordings, we study neural measures of speech intelligibility by manipulating intelligibility while keeping the acoustics strictly unchanged. Acoustically identical degraded speech stimuli (three-band noise-vocoded, ~20 s duration) are presented twice, but the second presentation is preceded by the original (nondegraded) version of the speech. This intermediate priming, which generates a "pop-out" percept, substantially improves the intelligibility of the second degraded speech passage. We investigate how intelligibility and acoustical structure affect acoustic and linguistic neural representations using multivariate temporal response functions (mTRFs). As expected, behavioral results confirm that perceived speech clarity is improved by priming. mTRFs analysis reveals that auditory (speech envelope and envelope onset) neural representations are not affected by priming but only by the acoustics of the stimuli (bottom-up driven). Critically, our findings suggest that segmentation of sounds into words emerges with better speech intelligibility, and most strongly at the later (~400 ms latency) word processing stage, in prefrontal cortex, in line with engagement of top-down mechanisms associated with priming. Taken together, our results show that word representations may provide some objective measures of speech comprehension.

Robust adhesive nanocomposite sponge composed of citric acid and nano clays modified cellulose for rapid hemostasis of lethal non-compressible hemorrhage

Massive bleeding control plays the main role in saving people's lives in emergency situations. Herein, modified cellulose-based nanocomposite sponges by polydopamine (PDA) and laponite nano-clay was developed to sturdily deal with non-compressible lethal severe bleeding. PDA accomplishes supreme adhesion in the bleeding site (∼405 kPa) to form strong physical barrier and seal the position. Sponges super porous (∼70 % porosity) and super absorbent capacity (48 g blood absorbed per 1 g sponge) by concentrating the blood cells and platelets provides the requirements for primary hemostasis. Synergistically, the nanocomposite sponges' intelligent chemical structure induces hemostasis by activation of the XI, IX, X, II and FVII factors of intrinsic and extrinsic coagulation pathways. Excellent hemostatic performance of sponges in-vitro was assessed by RBC accumulation (∼100 %), blood clotting index (∼10 %), platelet aggregation/activation (∼93 %) and clotting time. The nanocomposite sponges depicted super performance in the fatal high-pressure non-compressible hemorrhage model by reducing of >2, 15 and 3 times in the bleeding amount at New Zealand rabbit's heart and liver, and rat's femoral artery bleeding models, respectively compared to commercial hemostatic agents (Pvalue˂0.001). The in-vivo host response results exhibited biosafety with no systemic and significant local inflammatory response by hematological, pathological and biochemical parameters assessments.

Applications of the Order Reduction Optimization of the H-Infinity Controller in Smart Structures

In this paper, our strategy is to look for locally optimum answers to a non-smooth optimization problem that has been constructed to include minimization goals and restrictions for smart structures’ vibration suppression. In both theoretical analysis and practical implementation, it is widely recognized that designing multi-objective control systems poses a considerable challenge. In this study, we assess the effectiveness of this method by employing the open-source Matlab toolbox Hifoo 2.0 and juxtapose our findings with established industry standards. We start by framing the control problem as a mathematical optimization issue and proceed to identify the controller that effectively addresses this optimization. This approach introduces the potential application of intelligent structures in tackling the challenge of vibration suppression. This study makes use of the most recent version of the freely available application Hifoo which tries to study vibration suppression with the limits outlined above in the context of multi-objective controller design. A controller directive is initially set, allowing for a lower order.

A review of self-sensing ultra-high performance concrete: Towards next-generation smart structural materials

Today, the desired intelligent civil engineering structures urge the development of high-performance smart structural materials. Self-sensing ultra-high performance concrete (UHPC) emerged in 2014 based on the observation that the concrete structures rendered with randomly distributed conductive additives exhibit electromechanical effects under external loading, and the demand of UHPC from high-strength to sustainable, intelligent, and multifunctional. This smart structural material has since grown greatly and has been considered as one of the potential alternatives to ordinary self-sensing concrete. This paper presents a state-of-the-art review of self-sensing UHPC in terms of its raw materials, mixture design, manufacturing approaches, microstructural characteristics, mechanical properties, electrical resistivity, electromechanical properties, and applications, with a particular focus on stress-deformation/damage behaviour and changes in electrical conduction under external compression, tension, and bending. The novel uncertainties regarding electromechanical effects are thoroughly discussed from the perspective of the continuity of conductive network. Compared with ordinary self-sensing concrete, self-sensing UHPC exhibits significantly reduced porosity, exceptional strength, marginally lower stress/strain sensitivity, heightened damage sensitivity, insensitivity to humidity, and delayed cracking behaviour. In the end, this paper identifies research gaps, challenges, and recommendations in three aspects: (1) scale fabrication and cost control, (2) satisfactory and reliable properties, and (3) from smart structural materials to integrated components to civil engineering structures.

Neural network steering control algorithm for autonomous ground vehicles having signal time delay

An adaptive neural network–based steering control algorithm is proposed for yaw rate tracking of autonomous ground vehicles with in-vehicle signal time delay. The control system consists of two neural networks: the observer neural network and the controller neural network. The observer neural network adapts itself to the system dynamics during the training phase. Once trained, the observer neural network cooperates with the controller neural network, which constantly adapts itself during the control task. In this way, an adaptive and intelligent control structure is proposed. Through simulation studies, it has been shown that while a proportional-integral-derivative type steering controller fails to perform its control task in case of steering signal delay, the proposed control algorithm manages to adapt itself according to the control problem and achieves reference yaw rate tracking. The robustness of the control algorithm according to the signal delay magnitude has been demonstrated by simulation studies. A rigorous Lyapunov stability analysis of the control algorithm is also presented.