Intelligent Functions Research Articles

Solar Electric Wheel Chair By IJISRT

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

Development of Digit in Noise Test in Hindi.

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

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

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

A novel method to build knowledge graph models for the configuration and operation design of smart and connected industrial products

Abstract Smart and connected industrial products (SCIPs), characterized by their capabilities of self-monitoring, environment awareness, machine–machine/machine–human communication and collaboration, intelligent decision-making, etc., have become the fundamental elements for cyber-physical systems, digital twin, industrial internet of things, etc. Configuring the components in SCIPs and modeling their interaction and operation mechanisms are important during SCIPs design. However, existing product design methods were originally developed for none smart and connected products. This could limit the accuracy of SCIP modeling during the design stage and consequently, it may cause more reworks during the implementation stage of the designed SCIPs. In this regard, a SCIP configuration and operation design method is established, including (i) meta knowledge graph (KG)-based configuration of the components in the physical system and status monitoring system of a required SCIP, (ii) event-state swimlane flowchart-based analysis of the dynamic interaction, operation, and data monitoring mechanisms among the components, and (iii) event-state KG based modeling of the overall workflow, monitoring data self-updating and intelligent operation mechanisms of the SCIP. Compared with existing SCIP design methods, the work provides a specific method for not only the configuration of the static components in customized SCIPs, but also the dynamic interaction, data acquisition/storing/transmitting, and intelligent function implementation mechanisms of the configured SCIP using a kind of event-state KG. The event-state KG is both human-readable and computer-programmable, and it can self-update according to predefined reasoning algorithms during the operation of the SCIP. The configuration and operation design modeling of a robot-based grinding processing line is used as a case study.

Effects of quality of initial exposure stimuli on speech intelligibility

Listeners can use lexical information to drive adaptation to talker-specific speech characteristics with a small amount of exposure. This suggests that adaptation may be strongest when listeners are initially exposed to speech in a condition that facilitates word recognition. This was tested by examining the intelligibility of second-language (L2) speech-in-noise when stimuli were initially presented in Quiet versus low signal-to-noise ratio (SNR) conditions. Recordings of 10 L2 English talkers were tested. For each talker, 120 sentences were mixed with speech-shaped noise ranging from −4 dB to 8 dB in steps of 2 dB and in Quiet (15 sentences at each SNR). These stimuli were presented from lowest to highest SNR (forward; −4 dB to Quiet) in one group of L1 English listeners (n = 10/talker) and from highest to lowest SNR (reverse; Quiet to −4 dB) in the other (n = 10/talker). The results showed that early exposure to a clean signal led to better speech perception. At lower SNRs, word recognition accuracy in the reverse order was significantly higher than the forward order, resulting in a steeper slope of psychometric function of intelligibility in the forward versus reverse condition. The quality of initial speech input may influence talker adaptation, particularly in adverse listening conditions.

Some new fuzzy query processing methods based on similarity measurement and fuzzy data clustering

In relational and object-oriented database systems there is always data that is naturally fuzzy or uncertain. However, to deal with complex data types with fuzzy nature, these systems have many limitations. Therefore, in order to represent and manage fuzzy data, it is necessary to have a fuzzy interrogation system to facilitate non-expert users. To solve this challenge, the paper proposes two different approaches to increase the flexibility of the fuzzy interrogation system. Firstly, based on similarity measures and fuzzy logic, we develop three fuzzy query processing algorithms for single-condition and multi-condition cases such as FQSIMSC (Fuzzy Query Sim Single Condition), FQSIMMC (Fuzzy Query Sim Multi-Condition) and FQSEM (Fuzzy Query SEM). Secondly, we combine the fuzzy clustering algorithm EMC (Expectation maximization Coefficient) and the query processing algorithm that is based on fuzzy partitions FQINTERVAL (Fuzzy Query Interval). With this approach, we not only improve query processing cost but also support applications and devices equipped with intelligent interactive function that easily interacts with the fuzzy query system. The results of our theoretical and experimental analysis, it can be seen that both the proposed methods significantly reduce the processing time and memory space for a data set (extracted from UCI) that has a fuzzy and incomplete natural element with the resulting data size being optimal

Improving the Quality of Industrial Robot Control Using an Iterative Learning Method with Online Optimal Learning and Intelligent Online Learning Function Parameters

It is inevitable that the characteristics of a robot system change inaccurately or cannot be accurately determined during movement and are affected by external disturbances. There are many adaptive control methods, such as the exact linearization method, sliding control, or neural control, to improve the quality of trajectory tracking for a robot’s motion system. However, those methods require a great deal of computation to solve the constrained nonlinear optimization problem. This article first presents some techniques for determining the online learning function parameters of an intelligent controller, including two circuits: the inner circuit is an uncertain function component estimator to compensate for the robot’s input, and the outer circuit is an iterative learning controller and does not use a mathematical model of the robot with optimal online learning function parameters. The optimal condition is based on the model in the time domain to determine the learning function parameters that change adaptively according to the sum of squared tracking errors of each loop. As for the intelligent online learning function parameters, they closely follow the general model to stabilize the robot system, based on the principle of intelligent estimation of the uncertainty component and total noise. This method is built on Taylor series analysis for the state vector and does not use a mathematical model of the system at all. It allows feedback linearization, as well as intelligent stabilization of the system. This article’s content uses a 2-DOF flat robot implemented on MatlabR2022b software to verify the theory. These findings indicate that superior tracking performance is achievable.

Conflict-free joint decision by lag and zero-lag synchronization in laser network

With the end of Moore’s Law and the increasing demand for computing, photonic accelerators are garnering considerable attention. This is due to the physical characteristics of light, such as high bandwidth and multiplicity, and the various synchronization phenomena that emerge in the realm of laser physics. These factors come into play as computer performance approaches its limits. In this study, we explore the application of a laser network, acting as a photonic accelerator, to the competitive multi-armed bandit problem. In this context, conflict avoidance is key to maximizing environmental rewards. We experimentally demonstrate cooperative decision-making using zero-lag and lag synchronization within a network of four semiconductor lasers. Lag synchronization of chaos realizes effective decision-making and zero-lag synchronization is responsible for the realization of the collision avoidance function. We experimentally verified a low collision rate and high reward in a fundamental 2-player, 2-slot scenario, and showed the scalability of this system. This system architecture opens up new possibilities for intelligent functionalities in laser dynamics.

Recent Advances in Smart Windows Based on Photo-Responsive Liquid Crystals Featuring Phase Transition.

A smart window is an optical dimming device with intelligent functions that can control its relevant performances through external stimuli, achieving functions such as privacy protection and temperature regulation. Light is an ideal stimulus for regulating smart windows, which is noninvasive and allows self-adaptable manipulation of materials. This review highlights recent significant achievements in smart windows constructed by photo-responsive liquid crystals (LCs) systems that can undergo the transition between different phases. The smart windows based on photo-responsive LCs are used in a plethora of areas, including privacy protection, absorption glass, building decoration, energy saving, and climate modulation applications. The review concludes with a brief perspective on some significant challenges and opportunities for the future development of photo-responsive smart windows, which is crucial for expanding the applications of smart windows and improving their performances.

Memory‐Processing‐Display Integrated Hardware with All‐In‐One Structure for Intelligent Image Processing

AbstractEmpowering displays with intelligent functions enables their application in intelligent image processing and intelligent interactive displays, which is the combination of future display and artificial intelligence technologies. However, existing display technologies face significant processing and transmission burdens due to their conventional hardware separation architecture, which separates memory from the processor and display module from the processing module. To address these challenges, a highly integrated memory‐ processing‐display intelligent light‐emitting hardware (MPDIH) capable of information generation, memory, processing, and visualization is proposed. The MPDIH, based on an organic light‐sensitive layer and activated by ultraviolet light irradiation, exhibits unique photoinhibition behavior attributed to the reverse light‐induced electric field formed by the directional arrangement of photo‐generated excitons. This behavior enables dynamic regulation and autonomous learning during the device training process. Leveraging this phenomenon, intelligent image processing is successfully demonstrated, achieving a significant contrast improvement with a maximum enhancement of 261% compared to unprocessed raw signals. Furthermore, the memory‐processing‐display intelligent image processing scheme enables fashion MNIST image recognition using an artificial neural network, and achieves noticeably higher recognition accuracy (>89%) compared to the original fuzzy images (<59%). Consequently, the proposed memory‐processing‐display intelligent light‐emitting hardware holds promise for future applications in integrated photonic systems and interactive artificial intelligence displays.

Platform Supporting Intelligent Human–Machine Interface (HMI) Applications for Smart Machine Tools

As the Internet of Things, artificial intelligence, and the fourth industrial revolution advance, smart factories and machines increasingly gain intelligent features that enable the integration of more sophisticated functionalities. Approaches to achieving this intelligence involve both internal systems, such as human–machine interface (HMI), and external systems, such as big data platforms and cloud services. Although current research leans toward studying external systems, accomplishing intelligent functions through such means poses more challenges in achieving real-time responses during machining processes than using internal systems. When intellectualizing machine tools through internal HMI systems, three critical issues must be addressed. First, HMI functions are structured to depend on the HMI itself, leading to a ripple effect where a problem occurring in one HMI function impacts the entire system. Second, owing to differences in development tools and programming languages, the interconnectivity between functions developed by multiple stakeholders to be loaded onto the HMI may suffer, leading to potential inefficiencies and increased maintenance costs. Third, although various types of computer numerical control (CNC) machines need to communicate with the HMI function, the diverse communication methods and development tools used by each CNC manufacturer result in identical intelligent functions being developed separately for each CNC type. To address these challenges, this study proposes an innovative HMI platform capable of executing and developing various intelligent functions. The HMI platform and its major components are designed and implemented through component-based development (CBD). Subsequently, the performance and effectiveness of the platform are validated using quality attribute scenarios.

Differential perovskite hemispherical photodetector for intelligent imaging and location tracking

Advanced photodetectors with intelligent functions are expected to take an important role in future technology. However, completing complex detection tasks within a limited number of pixels is still challenging. Here, we report a differential perovskite hemispherical photodetector serving as a smart locator for intelligent imaging and location tracking. The high external quantum efficiency (~1000%) and low noise (10−13 A Hz−0.5) of perovskite hemispherical photodetector enable stable and large variations in signal response. Analysing the differential light response of only 8 pixels with the computer algorithm can realize the capability of colorful imaging and a computational spectral resolution of 4.7 nm in a low-cost and lensless device geometry. Through machine learning to mimic the differential current signal under different applied biases, one more dimensional detection information can be recorded, for dynamically tracking the running trajectory of an object in a three-dimensional space or two-dimensional plane with a color classification function.

Optimal ALT threshold for the automated diagnosis of MASLD: A population-based study using iLFT

Introduction and ObjectivesEarly diagnosis of metabolic dysfunction-associated steatotic liver disease (MASLD), especially with advanced fibrosis, is crucial due to the increased risk of complications and mortality. Serum alanine aminotransferase (ALT) is commonly used; however, many patients have normal ranges (<55 U/L) who may remain undetected. We investigated the clinical implications of a lower ALT cut-off (>30 U/L) using intelligent liver function testing (iLFT) to identify MASLD patients with and without advanced fibrosis in primary care. Materials and MethodsAll patients entering the iLFT diagnostic pathway had liver aetiological screening investigations if ALT >30 U/L. In those with MASLD the proportions with and without advanced fibrosis at different ALT thresholds: 31–41 U/L, 42–54 U/L and ≥55 U/L were compared. Results16,373 patients underwent iLFT between March 2016 to April 2022. 762 (5 %) patients had MASLD with abnormal fibrosis scores, while 908 (6 %) had MASLD with normal fibrosis scores. 428 (56 %) patients were assessed in liver clinics, where 169 (39 %) had evidence of fibrosis. Of these, 22 (13 %) had ALT 31–41 U/L, 31 (18 %) had ALT 42–54 U/L and 116 (69 %) had ALT ≥55 U/L. 145 (86 %) patients had advanced fibrosis or cirrhosis, where 20 (14 %) had ALT 31–41 U/L, 28 (19 %) had ALT 42–54 U/L and 97 (67 %) had ALT ≥55 U/L. Conclusions33 % of MASLD patients with advanced fibrosis or cirrhosis had ALT 31–54 U/L, who would have been missed using the conventional ALT range. This suggests that lowering the ALT cut-off improves diagnosis of MASLD with advanced fibrosis in primary care.

Automated assembly of products by a robot-manipulator with dynamometric control of screw joint tightening

In this work, we develop a conceptual scheme for a robotic complex to perform the installation of parts and their fastening using threaded joints by a robot manipulator. A test bench was created on the basis of a KUKA KR6 R900 industrial robot-manipulator. The robot control software was implemented using the KUKA Robot Language. The proposed actuators are the authors' original development. Two variants of the actuator with screw tightening control are proposed. The first actuating device uses the rotational motion of the robot hand flange to tighten the screws. The second actuating device is equipped, on one side, with a gripper for mounting parts on the product and, on the other side, with a drive tool with a predetermined tightening torque limit. It is shown that both actuating devices ensure a complete cycle of screwing a fastener into a workpiece, from engaging to tightening with the required torque. In the first device, the tightening force is controlled with a strain gauge, the signal from which is processed by an Arduino Mini microcontroller located in a rotating device. The signal is forwarded wirelessly to a stationary controller that sends a stop signal to the robot. It was experimentally established that, due to the presence of the wireless interface, some delay is observed between reaching the torque limit value and stopping the robot rotation during signal transmission. As a result, the value of the actually reached torque may exceed the set value by 60%. In the second device, where torque limitation is based on the current in the drive motor, the absolute error of the torque setting does not exceed 0.8 N·m in the range from 0 to 25 N·m or 3.03% (the error of standard torque wrenches is about 4%). In order to meet the requirements of state-of-the-art cyber-physical production systems, the proposed complex should be complemented with intelligent functions of controlling the process of tightening screw joints on the basis of machine learning methods.

Direct 4D printing of functionally graded hydrogel networks for biodegradable, untethered, and multimorphic soft robots

Recent advances in functionally graded additive manufacturing (FGAM) technology have enabled the seamless hybridization of multiple functionalities in a single structure. Soft robotics can become one of the largest beneficiaries of these advances, through the design of a facile four-dimensional (4D) FGAM process that can grant an intelligent stimuli-responsive mechanical functionality to the printed objects. Herein, we present a simple binder jetting approach for the 4D printing of functionally graded porous multi-materials (FGMM) by introducing rationally designed graded multiphase feeder beds. Compositionally graded cross-linking agents gradually form stable porous network structures within aqueous polymer particles, enabling programmable hygroscopic deformation without complex mechanical designs. Furthermore, a systematic bed design incorporating additional functional agents enables a multi-stimuli-responsive and untethered soft robot with stark stimulus selectivity. The biodegradability of the proposed 4D-printed soft robot further ensures the sustainability of our approach, with immediate degradation rates of 96.6% within 72 h. The proposed 4D printing concept for FGMMs can create new opportunities for intelligent and sustainable additive manufacturing in soft robotics.

Evaluation of intelligent packaging functions of black carrot extract-infused polyvinyl alcohol nanofibers

In this study, we aimed to evaluate the characteristic changes in polyvinyl alcohol-based nanofibers after being stored with black sea salmon fillets. For this purpose, electrospun nanofibers were produced as control (PVA), black carrot extract-incorporated (PVAB), and extract+SnO2 incorporated (PVASN), and their properties were compared. Morphological results showed the formation of regular ultrafine nanostructures, and differences between nanofiber samples were evaluated by measuring fiber diameters. Nanofiber sizes increased with the addition of black carrot and SnO2. Elemental analysis was performed to obtain information about the concentration in the nanofibers. Different O and Sn concentrations indicated that important constituents of anthocyanins and SnO2 were attached to the samples. The location of C, O, Sn atoms was determined by Energy Spectrum Analysis (EDS) color mapping, which confirmed the attachment of Sn in the PVASN sample. To elucidate the relationship between spoilage and the absorption of volatiles from smoked salmon fillets, nanofibers and fillets were kept together in a petri dish at room temperature. After being stored with salmon meat, the PVASN sample showed fewer entanglements without any defects. Upon exposure to volatiles, molecular structures changed, and water contact angle values decreased. X-Ray Diffraction (XRD) results indicated the state of the polymer matrix and SnO2. Chemical bond interactions with released volatiles effected the peak parameters of nanofibers. 3D and 2D surface topographical images of PVASN were also compared before and after storage using a profilometer. This study's results showed that functionalized nanofibers with extract-SnO2 could be applied as an intelligent food packaging layer, providing valuable data about the potential usage of electrohydrodynamic processing.

Virtual-Reality-Based Supermarket for Intellectual Disability Classification, Diagnostics, and Assessment

Possible symptoms of intellectual disability (ID) include delayed physical development that becomes more pronounced as the disability progresses, delayed development of gross and fine motor skills, sensory perception problems, and difficulty grasping the integrity of objects. Although there is no cure or reversal, research has shown that extensive training and learning can lead to easier social integration, but the human demands of diagnosis and the cost of training often result in overburdened families of origin, an unmanageable workload for teachers, and high social costs. Therefore, it is important to conduct efficient, effective, and economical assessments in a safe and reproducible training environment. Currently, the assessment of intellectual disability relies on intelligence tests such as the Wechsler Intelligence Scale (WIS) and the Vineland Adaptive Behavior Scale (VABS). With the rapid development of virtual reality (VR) and machine learning (ML), we created a virtual supermarket and then collected data in three areas, including eye movements, brain waves, and behaviors. We also propose an intelligent executive function evaluation using ML to develop a more objective and automatic evaluation model based on real data through physiological data obtained from user reflections. Statistical analysis of the obtained data showed that some data metrics derived from behavioral information differed significantly between ID patients and healthy participants. This shows that it is possible to perform classification through neural networks, even at multiple levels, which may prove effective for vocational training through VR.

Integrated Actuation and Sensing: Toward Intelligent Soft Robots.

Soft robotics has received substantial attention due to its remarkable deformability, making it well-suited for a wide range of applications in complex environments, such as medicine, rescue operations, and exploration. Within this domain, the interaction of actuation and sensing is of utmost importance for controlling the movements and functions of soft robots. Nonetheless, current research predominantly focuses on isolated actuation and sensing capabilities, often neglecting the critical integration of these 2 domains to achieve intelligent functionality. In this review, we present a comprehensive survey of fundamental actuation strategies and multimodal actuation while also delving into advancements in proprioceptive and haptic sensing and their fusion. We emphasize the importance of integrating actuation and sensing in soft robotics, presenting 3 integration methodologies, namely, sensor surface integration, sensor internal integration, and closed-loop system integration based on sensor feedback. Furthermore, we highlight the challenges in the field and suggest compelling directions for future research. Through this comprehensive synthesis, we aim to stimulate further curiosity among researchers and contribute to the development of genuinely intelligent soft robots.

Quantifying morphologies of developing neuronal cells using deep learning with imperfect annotations

The functionality of human intelligence relies on the interaction and health of neurons, hence, quantifying neuronal morphologies can be crucial for investigating the functionality of the human brain. This paper proposes a deep learning (DL) based method for segmenting and quantifying neuronal structures in fluorescence microscopy images of developing neuronal cells cultured in vitro. Compared to the majority of supervised DL-based segmentation methods that heavily rely on creating exact corresponding masks of neuronal structures for the preparation of training samples, the proposed approach allows for imperfect annotation of neurons, as it only requires tracing the centrelines of the neurites. This ability accelerates the preparation of training data by several folds. Our proposed framework is built on a modified version of PSPNet with an EfficientNet backbone pre-trained on the CityScapes dataset. To handle the imperfectness of training samples, we incorporated a weighted combination of two loss functions, namely the Dice loss and Lovász loss functions, into our network. We evaluated the proposed framework and several other state-of-the-art methods on a published dataset of approximately 900 manually quantified cultured mouse neurons. Our results indicate a close correlation between the proposed method and manual quantification in terms of neuron length and the number of branches while demonstrating improved analysis speed. Furthermore, the proposed method achieved high accuracy in neuron segmentation, as evidenced by the evaluation of the neurons’ length and number of branches.

Achieving Organizational Effectiveness through Machine Learning Based Approaches for Malware Analysis and Detection

Introduction: as technology usage grows at an exponential rate, cybersecurity has become a primary concern. Cyber threats have become increasingly advanced and specific, posing a severe risk to individuals, businesses, and even governments. The growing complexity and sophistication of cyber-attacks are posing serious challenges to traditional cybersecurity methods. As a result, machine learning (ML) techniques have emerged as a promising solution for detecting and preventing these attacks. Aim: this research paper offers an extensive examination of diverse machine learning algorithms that have the potential to enhance the intelligence and overall functionality of applications. Methods: the main focus of this study is to present the core principles of distinct machine learning methods and demonstrate their versatile applications in various practical fields such as cybersecurity systems, smart cities, healthcare, e-commerce, and agriculture. By exploring these applications, this paper contributes to the understanding of how machine learning techniques can be effectively employed across different domains. The article then explores the current and future prospects of ML in cybersecurity. Results: this paper highlights the growing importance of ML in cybersecurity and the increasing demand for skilled professionals who can develop and implement ML-based solutions. Conclusion: overall, the present article presents a thorough examination of the role of machine learning (ML) in cybersecurity, as well as its current and future prospects. It can be a valuable source of information for researchers, who seek to grasp the potential of ML in enhancing cybersecurity