Prototype System Research Articles

Torque Measurement System Design for Step Motor 23HS30-3004S

This article describes the methodology employed in the development of a prototype system designed for torque measurement. The system consists of two components. Α rotating component, the transmitter, and a fixed component, the receiver. The torque data, obtained from a strain gauge bridge, are processed and transmitted to the receiver through a transformer-operated device. Rotary transformers are deployed to supply power to electronics that are mounted on shafts. This ensures continuous and stable data transmission without requiring physical connections. Additionally, the accuracy of the design model is verified through design equations and simulation results, reinforcing the system’s feasibility and reliability for real-world applications.

Contact ion-pair SN2 reactions activated by Lewis Base Phase transfer catalysts

We present experimental probes of contact ion-pair (CIP) SN2 reactions for simplest prototype systems by 19F-NMR spectroscopy. This study provides crucial evidences for the reactions of CIP metal salts facilitated by Lewis base phase transfer catalysts (PTCs) [2,2,2]-cryptand, 18-crown-6, pentaethylene glycols (pentaEGs) and BINOL–based pentaEG. The 19F-NMR spectra of MF (M = K, Cs) ion-pairs in various solvents are used as fingerprinting tools to identify the CIP CsF/PTC/substrate complexes in SN2 reactions. Examination of the prototype reactions demonstrates that the novel CIP mechanism, which is in sharp contrast to the conventional perspectives, may account for the observed phenomenal efficiency of the SN2 processes using the alkali metal salts under the influence of various PTCs. In this extremely efficient and selective protocol of wide applicability, the CIP MF is activated by the Lewis base PTCs acting on the counter-cation M+ to drastically mitigate the latter’s retarding Coulomb forces on the adjacent nucleophile F-, with or without the participation of hydrogen bonds.

Structural Modulation and Enhanced Magnetic Ordering in Incommensurate K1-xCrSe2 Crystals.

Layered delafossite-type compounds and related transition metal dichalcogenides, characterized by their triangular net structures, serve as prototypical systems for exploring the intricate interplay between crystal structure and magnetic behavior. Herein, we report on the discovery of the compound K1-xCrSe2 (x ≈ 0.13), an incommensurately modulated phase. Single crystals of this compound were grown for the first time using a K/Se self-flux. We find a monoclinic crystal structure with incommensurate modulation that can be rationalized by a 3 + 1-dimensional model. This modulation compensates for the under-stoichiometry of K cations, creating pronounced undulations in the CrSe2 layers. Our anisotropic magnetization measurements reveal that K1-xCrSe2 undergoes a transition to a long-range magnetically ordered state below TN = 133 K, a temperature 1.6 to 3.3 times higher than in earlier reported KCrSe2 compounds. Our findings open new avenues for tuning the magnetic properties of these layered materials through structural modulation.

Design and Development of a Cost Effective WebVR Commerce System Prototype

This project focuses on the design and development of a cost-effective WebVR commerce system prototype to enhance the online shopping experience. Despite the rapid growth of E-commerce, platforms face challenges such as limited interactivity, static product presentations, and lengthy purchasing procedures, which often lead customers to abandon online shopping in favor of physical stores. The proposed WebVR system leverages virtual reality to address these issues, providing an immersive and interactive shopping experience. By enabling users to visualize and explore products in a 3D virtual environment, the system bridges the gap between online and in-store shopping. However, virtual reality often requires costly hardware and powerful workstations to process the positioning data. Therefore, this project focuses on utilizing affordable technologies and frameworks to create a seamless and accessible solution, making WebVR a more practical option for a wider audience.

High-speed spectral imaging via multispectral pulse illumination and temporal-spectral decoupling.

Spectral imaging (SI) faces inherent trade-offs among spatial, temporal, and spectral resolutions due to limited bandwidth. Current high-speed SI systems, relying on fast modulation or specific cameras, compromise spatial resolution or suffer high complexity and cost. This work presents a low-cost, high-speed SI scheme achieving up to 240 frames per second (fps) and even μs-resolved multispectral observation. This work reveals the inherent temporal-spectral redundancy in SI videos and reports an active temporal-spectral coupling (TSC) and decoupling (TSD) strategy for high-speed SI. We established a prototype system using a snapshot SI camera and a multispectral LED array. The LEDs sequentially illuminate the scene channel by channel in one exposure for TSC acquisition. Each channel of the measurement corresponds one-to-one to an illumination moment. A transformer-based network is applied to decouple textural and spectral information from the measurement and re-couple them to reconstruct images across all channels and time instances. Experiments validated that the reported framework can successfully record μs-resolved multispectral videos.

Car Rental System

This Paper gives a quick summary of web based car rental system , where you can rent a car from this website. Customer can rent what ever car they want for a short period of time . It is safe and secure website cause we use some verification process example: information of aadhar card number and driver licence to ensure the driver who is driving the car is above 18 age and has driver licence . this website include the feature like customer login ,password ,check availability od vehicle ,and direct chat with the admin, make payment through credit or debit card .for admin the features are admin login , adding cars ,deleting the cars ,accept or reject the request from the customer. This set of less time consuming and slightly secured when compared to the traditional method ,but still requires the admin to manually key in all client information .This study are develop the vehicles to rent online which include cars, motorcycles and vans and etc. to enhance the coverage of online vehicles rental services in Nashik and finally is to improve the online system for increasing the brands and models over hales for cars and motorcycles and vans to rent online Nashik .other companies are trying to revolutionize their business to adapt the mobile technology that been growing rapid in recent years .Therefore in the work the project aimed to propose a mobile car rental system prototype that is secured and allowed the cars to make reservation of the vehicle that desired. The mobile car rental system includes features as register and login page ,list of vehicles and database related to mobile car rental system

Opportunities for gas-phase science at short-wavelength free-electron lasers with undulator-based polarization control

Free-electron lasers (FELs) are the world's most brilliant light sources with rapidly evolving technological capabilities in terms of ultrabright and ultrashort pulses over a large range of photon energies. Their revolutionary and innovative developments have opened new fields of science regarding nonlinear light-matter interaction, the investigation of ultrafast processes from specific observer sites, and approaches to imaging matter with atomic resolution. A core aspect of FEL science is the study of isolated and prototypical systems in the gas phase with the possibility of addressing well-defined electronic transitions or particular atomic sites in molecules. Notably for polarization-controlled short-wavelength FELs, the gas phase offers new avenues for investigations of nonlinear and ultrafast phenomena in spin-orientated systems, for decoding the function of the chiral building blocks of life as well as steering reactions and particle emission dynamics in otherwise inaccessible ways. This roadmap comprises descriptions of technological capabilities of facilities worldwide, innovative diagnostics and instrumentation, as well as recent scientific highlights, novel methodology, and mathematical modeling. The experimental and theoretical landscape of using polarization controllable FELs for dichroic light-matter interaction in the gas phase will be discussed and comprehensively outlined to stimulate and strengthen global collaborative efforts of all disciplines. Published by the American Physical Society 2025

Knowledge-driven semantic converting method of multimodal models toward a geospatial perspective

ABSTRACT In the virtual geographic environment, conducting status analysis on urban structures and similar objects is crucial for enhancing their detailed management level. However, it is challenging to directly convert the same object across various software systems with different modalities (such as spatial analysis, BIM design, numerical simulation etc.). Therefore, the effective conversion of multi-modal models becomes pivotal. Due to the characteristics of inconsistent spatial description and complex association relationship among multimodal models, resulting in low knowledge reuse rate, poor accuracy of unit mapping, and low efficiency of state sharing in the process of model conversion. Aiming at these problems, this article delves into the knowledge-driven semantic conversion techniques for multi-modal models from a geospatial viewpoint. The mapping relationships between multimodal models in terms of spatial, geometric, and semantic information were first clarified. Subsequently, a structural matching template based on knowledge reuse was established, and a knowledge-guided algorithm for multimodal model transformation was designed. Finally, using a suspension bridge as a case study, a prototype system was developed and experimental analysis was conducted. The experimental results show that the method proposed in this article can accurately convert between BIM models, numerical analysis models, and GIS scene models, with spatial coordinate accuracy controlled within 1 mm and a conversion efficiency increase of more than 10 times. This can effectively enhance the integrated performance of models in applications such as digital geospatial twin scenarios.

Mining population opinion about local police

Abstract Sentiment analysis, or opinion mining, is an important task of natural language processing (NLP) that extracts opinions, attitudes, and emotions from text. With the growth of digital platforms like blogs and social networks, opinion mining has become a key tool for organizations to understand public sentiment. In recent research, machine learning and lexicon-based approaches have been applied to analyze sentiments. Our work specifically focuses on national security, where sentiment analysis offers crucial insights into local opinions, helping authorities gauge public mood. As part of our research, we developed the Public Sensing about Police Platform, a prototype system designed to analyze emotions from social networks. This system generates dashboards for law enforcement and security agencies, providing actionable intelligence for public safety. Our findings show that “Hate” was the most common emotion expressed in relation to police interventions, indicating widespread unpopularity of these actions and a resulting sense of insecurity among the public.

Intelligent Upgrading of the Localized GNSS Monitoring System: Profound Integration of Blockchain and AI

With the extensive application of the Global Navigation Satellite System (GNSS), the intelligent upgrading of the GNSS monitoring system is of particular significance. Traditional GNSS monitoring systems typically rely on a centralized architecture, which possesses certain drawbacks when it comes to data tampering, fault tolerance, and data sharing. This paper presents an intelligently upgraded localized GNSS monitoring system that integrates blockchain and artificial intelligence (AI) technology to achieve the deep integration of security, transparency, and intelligent processing of monitoring data. Firstly, this paper employs blockchain technology to guarantee the integrity and tamper-resistance of GNSS monitoring data and utilizes a distributed ledger structure to realize the decentralization of data storage and transmission, thereby enhancing the anti-attack capability and reliability of the system. Secondly, the LSTM model is utilized to analyze and predict the vast amount of monitoring data in real-time, enabling the intelligent detection of GNSS signal anomalies and deviations and providing real-time early warnings to optimize the monitoring effect. Based on this architecture, we also combine the trained model with smart contracts to realize real-time monitoring and early warnings of GNSS satellites. By integrating the security guarantee of blockchain and the intelligent analysis ability of AI, the localized GNSS monitoring system can offer more efficient and accurate data monitoring and management services. In the study, we constructed a prototype system and tested it in both simulated and real environments. The results indicate that the system can effectively identify and respond to GNSS signal anomalies, and enhance the monitoring accuracy and response speed. Additionally, the application of blockchain enhances the immutability and traceability of data, providing a solid foundation for the long-term storage and auditing of GNSS data. The introduction of AI algorithms, especially the application of the Long Short-Term Memory (LSTM) network in anomaly detection, has significantly enhanced the system’s ability to recognize complex patterns.

Prototype of AI-powered assistance system for digitalisation of manual waste sorting.

Global waste generation is projected to reach 3.40 billion tons by 2050, necessitating improved waste sorting for effective recycling and progress toward a circular economy. Achieving this transformation requires higher sorting intensity through intensified processes, increased efficiency, and enhanced yield. While manual sorting remains common, smaller plants often use positive sorting to recover recyclables, and larger plants combine automated systems with manual sorting. Negative sorting is employed to remove impurities and improve material quality. However, innovation in manual sorting has stagnated. Advances in Machine Learning and Artificial Intelligence offer transformative potential for waste management, with digitalisation and improved recyclate quality becoming priorities. Despite these trends, manual sorting is still largely treated as a digital black box. The presented research outlines the design of a novel, human-centric AI-powered assistance system to support sorting workers by enhancing decision-making and real-time assistance during the sorting process, driving the digitalisation of manual sorting. Potential use cases, system requirements, and essential components were explored. High-quality use case-specific data is essential for model training. Therefore, publicly available datasets were evaluated but found inadequate, necessitating use-case-specific data acquisition through near-industry-scale experiments. This data was used to train and develop key system components, such as object recognition, classification, and action recognition models. Results indicate that transfer learning with a balanced dataset is effective for waste-sorting applications. The classification model achieved 81% accuracy on an experimental acquired balanced dataset, outperforming the accuracy of the pre-trained model on its original dataset.

Neural Density Functional Theory of Liquid-Gas Phase Coexistence

We use supervised machine learning together with the concepts of classical density functional theory to investigate the effects of interparticle attraction on the pair structure, thermodynamics, bulk liquid-gas coexistence, and associated interfacial phenomena in many-body systems. Local learning of the one-body direct correlation functional is based on Monte Carlo simulations of inhomogeneous systems with randomized thermodynamic conditions, randomized planar shapes of the external potential, and randomized box sizes. Focusing on the prototypical Lennard-Jones system, we test predictions of the resulting neural attractive density functional across a broad spectrum of physical behavior associated with liquid-gas phase coexistence in bulk and at interfaces. We analyze the bulk radial distribution function g(r) obtained from automatic differentiation and the Ornstein-Zernike route and determine (i) the Fisher-Widom line, i.e., the crossover of the asymptotic (large distance) decay of g(r) from monotonic to oscillatory, (ii) the (Widom) line of maximal correlation length, (iii) the line of maximal isothermal compressibility, and (iv) the spinodal by calculating the poles of the structure factor in the complex plane. The bulk binodal and the density profile of the free liquid-gas interface are obtained from density functional minimization and the corresponding surface tension from functional line integration. We also show that the neural functional describes accurately the phenomena of drying at a hard wall and of capillary evaporation for a liquid confined in a slit pore. Our neural framework yields results that improve significantly upon standard mean-field treatments of interparticle attraction. Comparison with independent simulation results demonstrates a consistent picture of phase separation even when restricting the training to supercritical states only. We argue that phase coexistence and its associated signatures can be discovered as emerging phenomena via functional mappings and educated extrapolation. Published by the American Physical Society 2025

A Methodology for Online Situational Awareness Provision in a Business Entity

This paper presents a practical approach to building situational awareness at a critical infrastructure entity and its implementation in supporting security management. It outlines the main factors for achieving a high level of situational awareness and ensuring the safety and continuity of business objectives. Among other things, situational awareness requires precise identification and understanding of multilateral dependencies of infrastructure, services, and processes executed by the entity and services provided by external businesses, resulting from the strong interrelationship of the critical infrastructure sectors and the sharing of resources. Accordingly, this paper presents a cyberspace modeling methodology that supports an in-depth analysis of the causes and consequences of threat proliferation in a complex infrastructure–business environment and its implementation in a Situational Awareness Management System (SAMS). An emphasis is placed on threat propagation analysis and dynamic risk assessment mechanisms and how they are used to identify and take preemptive actions protecting or limiting the scope of the threats’ propagation. The paper concludes with insights from a pilot implementation of the system prototype and directions for further work.

MINDPRES: A Hybrid Prototype System for Comprehensive Data Protection in the User Layer of the Mobile Cloud

MINDPRES: A Hybrid Prototype System for Comprehensive Data Protection in the User Layer of the Mobile Cloud

Innovative Capacitive Wireless Power System for Machines and Devices

This article deals with the design, development, and analysis of a wireless power transfer system prototype based on capacitive coupling. The system is intended for the continuous charging of a suspended mine drivetrain. It consists of a resonant inverter, primary and secondary matching circuits, a suitable capacitive coupler, and a rectifier with a load, ultimately serving as a battery charger for a mobile energy storage device. The system successfully achieved the target output voltage of 320 V and a charger output power of 2 kW at an operating frequency of 300 kHz. Additionally, the total system efficiency was at the level of 60%, ensuring that the RMS voltages on passive components remained below 3 kV.

Heart Rate Variability Estimation Based on RFID Tag-Pair in Dynamic Environments

With the rapid development of smart health care, accurate heart rate variability (HRV) estimation for the early detection of diseases has become a hot research topic. Advanced work uses the wireless signal to estimate the heartbeat in a contact-free way, which usually cannot separate multiple users or work in a dynamic environment. In this article, we propose a lightweight heartbeat-sensing method based on RFID tag pairs, which focuses on HRV extraction in a more general sensing scenario. Based on the tag-pair design, we build a novel heartbeat and respiration model to describe the signal relationship between the two tags from the time and space domains. Based on the model, we propose a Calibrated Temporal-Spatial IQ-Shaping-based signal cancellation algorithm to cancel the respiration and extract the heartbeat. To remove the interference in dynamic measurement, we build an IQ-based signal model via a Principal Component Analysis-based interference estimation. To reduce the statistical error in HRV extraction, we further design a neural network to predict the HRV index. We have implemented a system prototype in a real environment with COTS RFID devices. Extensive experiments show that our system can achieve a median RMSSD error of 7.51 ms, which satisfies the medical demand in HRV measurement.

Smart Governance System’s Design to Monitor the Commitments of Bio-Business Licensing in Indonesia

Some business license commitments in online single submission (OSS) application currently only consist of an independent statement from the business actor, and there is no time limit for fulfilling the license, especially for low-risk bio-business licenses. Therefore, a system design is needed to monitor the fulfillment of business license commitments and to provide confidence to the public regarding the business provided. The system design started with discussing the importance of smart governance systems, defining the requirements for monitoring the commitment of bio-business licenses, selecting documents, determining important words and document types, and proposing a design. This research produces a prototype of a business license monitoring system. This smart governance system checks the validity of business license documents. This study provides new insights into how smart governance system can be implemented in bio-business licensing. This study also considers the thousands of government applications that currently exist and the desire to provide one solution that contains many functions that can fulfill the many needs of business actors for simple and transparent business licenses.

Повышение конкурентоспособности отечественного сельхозмашиностроения посредством управления рисками

The study of competitiveness and risk management issues is currently relevant for many industries and areas of functioning of the agro-industrial complex, including agricultural engineering. The article calculates the level of losses of agricultural engineering enterprises caused by the disparity of economic relations in the agro-industrial complex. Based on this, an assessment is given of the direct and reverse impact of local and industry risks on the functioning of agricultural engineering enterprises, which made it possible to develop a prototype of a risk management system at agricultural engineering enterprises. The study identified the prerequisites for the formation of a harmonious and effective organizational and economic mechanism for the functioning of agricultural engineering enterprises based on a set of scientific approaches. The authors developed a conceptual scheme of the risk management system at agricultural engineering enterprises. The study resulted in proposals for improving the instruments of state regulation of the risk management system of an enterprise in the industry.

Neural Network Simulator of Gas Turbine Engines for Prototyping and Debugging of Control Systems on Unsteady Modes

Neural Network Simulator of Gas Turbine Engines for Prototyping and Debugging of Control Systems on Unsteady Modes

Modeling, Design, and Laboratory Testing of a Passive Friction Seismic Metamaterial Base Isolator (PFSMBI).

This paper focuses on the theoretical and analytical modeling of a novel seismic isolator termed the Passive Friction Mechanical Metamaterial Seismic Isolator (PFSMBI) system, which is designed for seismic hazard mitigation in multi-story buildings. The PFSMBI system consists of a lattice structure composed of a series of identical small cells interconnected by layers made of viscoelastic materials. The main function of the lattice is to shift the fundamental natural frequency of the building away from the dominant frequency of earthquake excitations by creating low-frequency bandgaps (FBGs) below 20 Hz. In this configuration, each unit cell contains an inner resonator that slides over a friction surface while it is tuned to vibrate at the fundamental natural frequency of the building. This resonance enhances the energy dissipation capacity of the PFSMBI system. After deriving the governing equations for four selected lattice configurations (i.e., Cases 1-4), a parametric study is performed to optimize the PFSMBI system for a wide range of harmonic ground motion frequencies. In this study, we examine how key parameters, such as the mass ratios of the cells and resonators, tuning frequency ratios, the number of cells, and the coefficient of friction, affect the system's performance. The PFSMBI system is then incorporated into the dynamic model of a six-story base-isolated building to evaluate its effectiveness in reducing the floor acceleration and inter-story drift under actual earthquake ground motion records. This dynamic model is used to investigate the effect of stick-slip motion (SSM) on the energy dissipation performance of a PFSMBI system by employing the LuGre friction model. The numerical results show that the optimized PFSMBI system, through its lattice structure and frictional resonators, effectively reduces floor acceleration and inter-story drift by leveraging FBGs and frictional energy dissipation, particularly when SSM effects are properly accounted for. Finally, a small-scale prototype of the PFSMBI system with two cells is developed to verify the effect of SSM. This experimental validation highlights that neglecting SSM can lead to an overestimation of the energy dissipation performance of PFSMBI systems.