MTS Systems (United States)

Human-machine communication is becoming more and more complicated, which requires conversational agents that will be able to constantly improve themselves and be aware of the situation. The conventional models of dialogue are based on fine-tuning that is not dynamic and they do not have any means of long-term adaptation in the process of real-life communication. This paper attempts to overcome this drawback by presenting a novel dynamic meta learning network named Cognitive Gradient Modulation Network (CGMN) - which enables the autonomous learning of conversational behavior. CGMN encompasses a Meta-Cognition Engine to trace linguistic consistency, a Gradient Modulation Layer to do localized parameter change-offs and an Evolutionary Memory Bank that maintains the history of context adaptation. The model uses user sentiment, coherence pattern, and task performance metrics through a Cognitive Feedback Reinforcement loop to improve the model dialogue strategies as a running record. CGMN uses lightweight gradient gating, opposed to the use of traditional retraining-based techniques, to allow efficient real-time fine tuning. The proposed model is tested experimentally to illustrate that the model has a high adaptability, long-term consistency, and the entropy of responses is less than in baseline transformer-based agents. All in all, CGMN provides a platform to build some self-regulating conversational systems that develop with the interaction of the user, forming the transition between a fixed large language model and a fully autonomous cognitive dialogue system. The suggested CGMN attained an overall accuracy of 95.6 percent, exhibiting enhanced flexibility and precision relative to current conversations models.

In the electric vehicle (EV) application, increasing power demand requires a reliable power supply to support continuous charging operation. In the previous optimisation framework, grid input source power quality, total harmonic distortions, the need to control energy management, and high EV load demand were limitation in existing work. To solve the limitation, a novel Multi Objective Evolutionary Algorithm for prediction based proper grid energy management with two modes of operation is proposed. An input source is a Grid and an alternative renewable PV source combined for an EV charging station, providing power to the EV and ensuring proper flow in both directions. Grid-to-Vehicle (G2V) charging provides power to charge an electric vehicle’s batteries, and Vehicle-to-Grid (V2G) charging returns power to the grid for discharge. The battery power is periodically evaluated at a PWM duty cycle over a specified time period (in seconds) during steady-state operation in two different modes. The Multi-Objective Evolutionary Algorithm analyses the input Grid and alternative solar renewable sources with proper charge and discharge functions. The EV load output, G2V and V2G output, SOC, and Battery energy management are calculated with an appropriate description of the dataset. The result improved energy management, effectively manages load demand, and improves battery backup for solar renewable sources and reliable EV output.

Traffic congestion in urban areas is an issue, like increased travel time, increased fuel use, and pollution. This study suggests an AI-Driven Adaptive Traffic Management System utilizing YOLOv8, a latest deep learning architecture, to dynamically change traffic light timing. The system takes inputs in real time from cameras and sensors mounted on intersections to evaluate and observe traffic conditions and provide predictive and adaptive changes for optimal traffic flow. Designed with a purpose to smoothly integrate into the existing infrastructure, the system is optimized for hardware to keep processing efficiency and restrict the deployment's impact. There are privacy protection mechanisms added to protect real-time video data, and infrared cameras capture images with greater clarity in low light conditions for smooth functioning. The proposed solution offers numerous benefits like reduced travel time, improved city government traffic regulation, and reduced emissions through optimized traffic flow. The system promotes social, economic, and environmental well- being while providing a cleaner urban environment with an enhanced overall quality of life through decreased fuel usage and air pollution.

Speech recognition systems display limited operational usability because their performance suffers under conditions with background noise. Acoustic disturbances lead to Traditional Convolutional Neural Networks (CNNs) losing their accuracy levels. The research establishes robust speech recognition using CapsNets that apply dynamic routing algorithms to keep audio feature spatial arrangements intact. The system consists of noise-proof preprocessing components which use multiple stacked capsule layers for specialized acoustic signal pattern processing. A testing procedure included system evaluation while varying noise levels (SNR from -5 to 20 dB with TIMIT and AURORA-4 datasets used for evaluation. Curatively applied CapsNets achieved 94.2% accuracy without noise but maintained 87.6% accuracy while facing noise interference at a 10 dB SNR rating. The system operated with only 12% performance decline under severe noise conditions which had a -5dB signal-to-noise ratio although standard CNN models demonstrated a 28% decrease in performance at this level. CapsNets offer prospects for creating voice recognition systems that can excel in operating within actual functional environments.

The capability for varied functionalization and precise control at the nanoscale are significant advantages of DNA nanostructures. In the assembly of DNA nanostructure, the surface-assisted growth method utilizing double-crossover (DX) tile structures facilitates nucleation at relatively low concentrations on the surface based on electrostatic interactions, thereby enabling crystal growth over large areas. However, in surface-assisted growth, the geometrical hindrance of vertical structures on the DX tile structure surface makes it challenging to conjugate DNA nanostructures into fabricated surfaces. Here, the surface-assisted growth method was employed to extend the DX tile growth for forming vertical structure arrays on the substrate, providing attachment sites for functionalization on uniformly covered substrates at the macroscopic scale. Additionally, the spacing of the vertical structure arrays was demonstrated to be controllable through the strategic design of the repeating unit tiles that construct the DX crystals.

The LunaNet Interoperability Specification (LNIS) has been created and released to the public, enabling international commercial and government lunar systems to have a common baseline to work together in forming complex lunar mission networks. The LNIS specifically defines a set of data service protocols that offer multiple options for different types of network users and providers to work together. This includes real-time frame delivery services similar to those classically used by missions, as well as both real-time and store-and-forward networking that will be important as the number of mission users and complexity of lunar mission operations increases. The NASA Lunar Communications Relay and Navigation Systems (LCRNS) project requires lunar relay satellite services to implement onboard processing and networking capabilities in some ways similar to LEO mega-constellations that offer Internet access, but also going beyond those capabilities to meet unique lunar mission needs (e.g. store-and-forward services, LNIS messaging, etc.). Onboard processing will be necessary for a number of different protocols that are included in the LNIS, including support for data transfer over lunar proximity radio links based on CCSDS framing and CCSDS Encapsulation Packets, networking via IP and DTN Bundle Protocol, a suite of DTN Convergence Layer Adapters, and LunaNet messaging services. This paper provides an overview of our work in contributing to and defining the LNIS service interfaces and LCRNS requirements related to onboard processing for data services. This includes exploration of the key aspects of lunar networking concept of operations, technology trade studies, protocols for trunking, applicability and scope of different protocols, messaging, security considerations, and realistic implementation and deployment constraints. While the technology for new lunar relay networks can build upon the recent advances in LEO constellations, there are unique new challenges related to network management, onboard processing hardware/software system constraints, security, and quality of service for human spaceflight missions. As multiple international systems are planned to be put online in the coming years based on the LNIS and derived data services, this work will have a lasting impact that supports the evolution from single-vehicle user missions into scenarios where many surface and orbital assets are able to collaborate over multi-hop networks operated by diverse providers.

Penelitian ini ingin mendeskripsikan tentang konsep pendidikan Akhlak dalam Islam. Penelitian ini menggunkan metode studi kepustakaan dengan mengumpulkan data dari berbagai sumber tertulis yang otoritatif. Hasil penelitian ini menunjukkan bahwa melalui pendidikan yang baik maka akan dihasilkan akhlak yang baik yang akan membangun karakter peserta didik. Pendidikan yang terbaik adalah pendidikan yang berlandaskan kepada Alquran dan hadis. Hal tersebut dikarena petunjuk yang terdapat dalam Alquran dan Hadis merupakan petunjuk dasar sebagi landasan dalam membangun pendidikan yang memiliki akhlak yang baik dan berkarakter.

Gathering data for cascading seismic and tsunami events is difficult due to space constraints in existing experimental facilities, the application of scaling laws for both the fluid and structure, and limitations of computational software in simulating multiple hazards within the same analysis. To overcome these limitations, this study demonstrates the feasibility of a real-time hybrid simulation testing method to enhance wave–structure interaction simulations. While real-time hybrid simulations have been utilized for structures subjected to seismic events for decades, considerations for hybrid testing make its application to hydrodynamic problems challenging, including testing in wet or submersible environments and satisfying similitude time constraints. A cylindrical bridge pier specimen connected to a three-dimensional numerical bridge model was subjected to cascading seismic and tsunami events deploying a three-loop hardware architecture for real-time hybrid simulation. The domain was partitioned such that the wave–structure interaction on the bridge pier was physically tested in a wave flume and coupled to a numerical model of the remaining bridge components. To simulate existing damage, seismic loading was numerically applied to the hybrid model prior to the physical wave loading. Results illustrate that this simulation approach is feasible for future investigations using real-time hybrid simulation.

Évről évre emelkedik az úgynevezett életmódfüggő betegségek (stroke, stressz, obesitas, magas vérnyomás, szívbetegség) kialakulásának száma, ezt azonban a szabadidősport és fizikai aktivitás növelésével csökkenteni lehet. A magyar nők és férfiak között egyaránt emelkedik a túlsúlyosok és az elhízottak aránya. Ennek egyik mérőszáma a BMI érték. Míg a 18–29 év közötti korosztályban az átlagos érték férfiaknál 25,0, nőknél 23,7 , addig a 70 és annál idősebb korosztályban a férfiak esetében ez 28,3-ra nőknél 28,6-ra emelkedik. Megfigyelhető, hogy a fiatalabb korosztályban a nők a BMI érték alapján egészségesebbek, viszont idős korra ez a jelenség megfordul. A 20 év felettiek közül a normál kategóriába azok tartoznak, akiknek a BMI értéke 18,5–24,9 között van. Túlsúlyosak a 25–29,9 közötti értékűek, e felett pedig már elhízottságról beszélünk. A túlsúlyból, elhízottságból eredő egészségügyi problémák nem csak egyéni, hanem társadalmi szinten is óriási gondot jelentenek. Számottevően nő a gazdasági, valamint az egészségügy leterheltségét mértéke is. Az elhízás gyakran a mozgáshiányos életmód következménye. Tartós fogyás pedig csak a testmozgás, diéta, súlykontroll hármasának együttes alkalmazásával érhető el. Az egészségtudatos szemléletet fiatal korban kell megalapozni. Kutatásunk egyik fő célja, hogy a résztvevőknek modern, népszerű, élvezhető és egyben hatékony mozgásformákat mutassunk be. Igyekeztünk a szakirodalmakat is segítségül hívni a bemutatásra kerülő mozgásformák kiválasztásához, hiszen a sikerhez szükséges ismerni, hogy általánosan milyen motiváció áll a rendszeresen sportoló emberek tevékenysége mögött. Érdemes a motivációkutatásokat alapul venni, mert ezek segítségével megtudhatjuk, hogyan lehet hatékonyan megszólítani a fiatalokat. Ötleteket kaphatunk ahhoz, hogy milyen üzenet küldésével lehet eredményesen motiválni és bevonni őket a szabadidősportban való részvételre. A rendszeres sportolás a stresszoldó hatása miatt is kiemelt fontossággal bír.

The tracking performance of an actuation transfer system in a real-time hybrid simulation (RTHS) frequently faces accuracy and robustness challenges under constraints and complicated environments with uncertainties. This study proposes a novel control approach based on the deep deterministic policy gradient algorithm in reinforcement learning (RL) combined with feedforward (FF) compensation, which emphasizes the implementation of shaking table control and substructure RTHS. The proposed method first describes the control plant within the RL environment. Then, the agent is trained offline to develop optimized control policies for interaction with the environment. A series of validation tests were conducted to assess the performance of the proposed method, starting with the dynamic testing of underwater shaking table control and then a virtual RTHS benchmark problem. For complex systems, such as controlling the underwater shaking table, the proposed algorithm, FF, and adaptive time series (ATS) compensation methods are compared under various water depths and motions. The results show better performance and wider broadband frequency applicability under different shaking table dynamic-coupling effects. Next, a controller based on the proposed method was designed by extending the virtual RTHS via the configuration of the control plant and substructure division, as provided in the RTHS benchmark problem. The proposed RL controller also improved the tracking accuracy and robustness of conventional FF compensators against unmodeled dynamics and perturbation uncertainties. This controller can be extended to further advanced control strategies as a component of model-based control methods.