Real-time User Research Articles

Impact of loading capability on optimal location of renewable energy systems distribution networks

A distribution system's network reconfiguration is the process of altering the open/closed status of sectionalizing and tie switches to change the topological structure of distribution feeders. For the last two decades, numerous heuristic search evolutionary algorithms have been used to tackle the problem of network reconfiguration for time-varying loads, which is a very difficult and highly non-linear efficiency challenge. This research aims to offer an ideal solution for addressing network reconfiguration difficulties in terms of a system for power distribution, to decrease energy losses, and increase the voltage profile. A hybrid Genetic Archimedes optimization technique (GAAOA) has also been developed to size and allocate three types of DGs, wind turbine, fuel cell and PV considering load variation. This approach is quite useful and may be used in many situations. This technique is evaluated for loss reduction and voltage profile on a typical 33-bus radial distribution system and a 69-bus radial distribution system. The system has been simulated using MATLAB software. The findings suggest that this approach is effective and acceptable for real-time usage.

Adaptive Human–Computer Interface Design for Supervision Task Based on User Attention and System State

The rapid development of computer information technology has increased the complexity of the human–computer interface, especially in supervision tasks. It is difficult for users to pay attention to multiple information and make decisions in real-time, which needs higher requirements for the design of a human–computer interface. The research designs a novel adaptive human–computer interface using the Adaptive Interface Design (AID) framework: Combining the real-time user attention and system state as the input layer of AID; Building a hybrid entropy attention allocation model as the decision layer of AID; Adjusting the real-time saliency of the information in the interface as the output layer of AID. The comparative experiments in supervision tasks are conducted to test the performance of the users in the designed adaptive interface and traditional interface. The results show that the designed adaptive human–computer interface can effectively improve the supervisors’ performance and reduce the risk of system failure.

DETERMINATION AND COMPARISON METHODS OF BODY POSITIONS ON STREAM VIDEO

Context. One of the tasks of computer vision is the task of determining the human body in the image. There are many methods to solve this problem, some are based on specific equipment (motion capture, kinect) and provide the highest accuracy, some give less accuracy but do not require additional equipment and use less computing power. But usually, such equipment has a high cost, so to ensure the low cost of developments designed to determine the body in the image, you should develop algorithms based on computer vision technology. These algorithms can then be applied to various fields to analyze and compare body positions for a variety of purposes.
 Objective. The aim of the work is to study the effectiveness of existing libraries to determine the human body position in the image, as well as methods for comparing the obtained poses in terms of speed and accuracy of determination.
 Methods. A set of libraries and pose comparison algorithms were analyzed for the purpose of developing a system for determining the correctness of exercise by the user in real time. OpenPose, PoseNet and BlazePose libraries were analyzed for their suitability in recognizing and tracking body parts and movements in real-time video streams. The advantages and disadvantages of each library were evaluated based on their performance, accuracy, and computational efficiency. Additionally, different pose comparison algorithms were analyzed. The effectiveness of each algorithm was evaluated based on their ability to accurately determine and compare body positions.
 As a result, the combination of BlazePose and weighted distance method can achieve the best performance in pose recognition, with high accuracy and robustness across a range of challenging scenarios. The weighted distance method can be further enhanced with techniques such as L2 normalization and pose alignment to improve its accuracy and generalization. Overall, the combination of the BlazePose library and weighted distance methods offers a powerful and effective solution for pose recognition, with high F1 index.
 Results. Existing models for determining poses have shown similar results in the quality of determination with a run-up of about 2%. When developing a cross-platform software product, the BlazePose library, which has an API for working directly in the browser and on mobile platforms, has a significant advantage in speed and accuracy. Also, as the library uses extended 33 keypoint topology it becomes applicable to a wider list of tasks. In the study of comparison methods, the greatest influence on the results was exerted by the quality of pose determination.
 Conclusions. Among the methods of comparison, the method of weighted distances showed the best results. The speed of position determination is inversely proportional to the quality of determination and significantly exceeds the recommended value – 40ms.

Testing a Web-Based Food Ordering System with the Application of Geolocation

In the modern restaurant industry, there is intense competition to achieve customer satisfaction through Service Excellence or Service Differentiation. One service advantage that restaurants can offer is a Food Ordering System. The Self-Service Food Ordering System is a solution for restaurants to enhance the customer experience by providing the freedom to order food independently. It is crucial for restaurants to protect this system from external misuse, such as fraudulent orders that can cause financial losses. One solution that can be implemented is the utilization of Geolocation. Implementing Geolocation allows restaurants to obtain real-time user location data with the user's consent, thereby providing better protection for the self-service food ordering system. Testing is necessary to ensure the proper functioning of the system, including testing its features, compatibility, and the geolocation component.

BMSQABSE: Design of a Bioinspired Model to Improve Security & QoS Performance for Blockchain-Powered Attribute-based Searchable Encryption Applications

Attribute-based searchable encryption (ABSE) is a sub-field of security models that allow intensive searching capabilities for cloud-based shared storage applications. ABSE Models require higher computational power, which limits their application to high-performance computing devices. Moreover, ABSE uses linear secret sharing scheme (LSSS), which requires larger storage when compared with traditional encryption models. To reduce computational complexity, and optimize storage cost, various researchers have proposed use of Machine Learning Models (MLMs), that assist in identification & removal of storage & computational redundancies. But most of these models use static reconfiguration, thus cannot be applied to large-scale deployments. To overcome this limitation, a novel combination of Grey Wolf Optimization (GWO) with Particle Swarm Optimization (PSO) model to improve Security & QoS performance for Blockchain-powered Attribute-based Searchable Encryption deployments is proposed in this text. The proposed model augments ABSE parameters to reduce its complexity and improve QoS performance under different real-time user request scenarios. It intelligently selects cyclic source groups with prime order & generator values to create bilinear maps that are used for ABSE operations. The PSO Model assists in generation of initial cyclic population, and verifies its security levels, QoS levels, and deployment costs under multiple real-time cloud scenarios. Based on this initial analysis, the GWO Model continuously tunes ABSE parameters in order to achieve better QoS & security performance levels via stochastic operations. The proposed BMSQABSE model was tested under different cloud configurations, and its performance was evaluated for healthcare deployments. Based on this evaluation, it was observed that the proposed model achieved 8.3% lower delay, with 4.9% lower energy consumption, 14.5% lower storage requirements when compared with standard ABSE models. It was able to mitigate Distributed Denial of Service (DDoS), Masquerading, Finney, and Sybil attacks, which assists in deploying the proposed model for QoS-aware highly secure deployments.

Digital Twin Concept, Method and Technical Framework for Smart Meters

Smart meters connect smart grid electricity suppliers and users. Smart meters have become a research hotspot as smart grid applications like demand response, power theft prevention, power quality monitoring, peak valley time of use prices, and peer-to-peer (P2P) energy trading have grown. But, as the carriers of these functions, smart meters have technical problems such as limited computing resources, difficulty in upgrading, and high costs, which to some extent restrict the further development of smart grid applications. To address these issues, this study offers a container-based digital twin (CDT) approach for smart meters, which not only increases the user-facing computing resources of smart meters but also simplifies and lowers the overall cost and technical complexity of meter changes. In order to further validate the effectiveness of this method in real-time applications on the smart grid user side, this article tested and analyzed the communication performance of the digital twin system in three areas: remote application services, peer-to-peer transactions, and real-time user request services. The experimental results show that the CDT method proposed in this paper meets the basic requirements of smart grid user-side applications for real-time communication. The container is deployed in the cloud, and the average time required to complete 100 P2P communications using our smart meter structure is less than 2.4 seconds, while the average time required for existing smart meter structures to complete the same number of P2P communications is 208 seconds. Finally, applications, the future development direction of the digital twin method, and technology architecture are projected.

Adaptive Symmetry Reference Trajectory Generation in Shared Autonomy for Active Knee Orthosis

Gait symmetry training plays an essential role in the rehabilitation of hemiplegic patients. Robotics-based gait training has been widely accepted by patients and clinicians. Reference trajectory generation for the affected side using the motion data of the unaffected side is an important way to achieve this. However, online generation of gait reference trajectory requires the algorithm to provide correct gait phase delay and could reduce the impact of measurement noise from sensors and input uncertainty from users. Based on an active knee orthosis (AKO) prototype, this work presents an adaptive symmetric gait trajectory generation framework for the gait rehabilitation of hemiplegic patients. Using the adaptive nonlinear frequency oscillators (ANFO) and movement primitives, we implement online gait pattern encoding and adaptive phase delay according to the real-time user input. A shared autonomy (SA) module with online input validation and arbitration has been designed to prevent undesired movements from being transmitted to the actuator on the affected side. The experimental results demonstrate the feasibility of the framework. Overall, this work suggests that the proposed method has the potential to perform gait symmetry rehabilitation in an unstructured environment and provide a kinematic reference for torque-assistance AKO.

Impact of fatigue as the primary determinant of functional limitations among patients with post-COVID-19 syndrome: a cross-sectional observational study

ObjectivesTo describe self-reported characteristics and symptoms of treatment-seeking patients with post-COVID-19 syndrome (PCS). To assess the impact of symptoms on health-related quality of life (HRQoL) and patients’ ability to work...

Smart proxy models art and future directions in the oil and gas industry: A review

This paper presents a review of the state of the art of proxy models application in the oil and gas industry from upstream, midstream, and downstream. The focus on proxy models is because they are the pillar of the digital twins. They compensate for the slow response of the numerical simulation models. Proxy models can get updated very quickly with new data compared to numerical models. The proxy models apply to cross-disciplines such as fluid mechanics, thermodynamics, and electronics. Proxy models are tools for the swift response to the process events when the cost of decision time is high. The history of the proxy model is discussed first followed by a review of the literature. First, a literature review and the history of the proxy model are covered. The objectives and uses of proxy models are then thoroughly described while the evolution and development process of proxy models are detailed. A few proxy model examples are provided to emphasize the significance of the proxy model and technical advancement. Regression, artificial neural networks, fuzzy logic, and support vector machines are the major approaches for delivering proxy models. Each method's strengths and drawbacks are addressed. These case studies show the need for data cleansing, data transformation to the appropriate domain, and a precise model verification and validation strategy for creating a trustworthy proxy model. Industry-wide, the gaps, and difficulties are addressed, including data accessibility, workflow, model health check, operability, and model uncertainty. A review of proxy models revealed that both technical solution acceptance and workflow robustness have greatly increased at the maturity level in proxy model applications in the oil and gas industry. However, the typical workflow is not being used, and many case studies show that crucial steps like verification and validation are not being taken. The key issue identified in the review was that proxy models are derived from analytical and numerical models, which is in contrast to proxy models, which by their nature rely on the data at hand to inform predictions and optimizations. The astounding potential of proxy models in prediction and optimization is still untapped, and more research is needed to improve model quality, incorporate data into the core of the proxy models, increase their robustness, and develop robust performance metrics to address and evaluate the performance, dependability, and timeliness of the proxy models. Currently, proxy models are secondary tools for optimization of the processes and decision making, while in the future outlook is to be the main tool not only in daily surveillance and monitoring but for the optimization process. In this paper, a best practice workflow for the proxy model is proposed for the oil and gas industry. The evolution of proxy models and their level of complexity is reviewed. Most published work on proxy models is built based on the synthetic data and simulation model output. In the applied workflow, most model validation and verification are missed or ignored. Except for a few published works on the real-time usage, the models are barely connected to real-time and they are used for static decision-making process other than real-time usage.

Sensitive product feature integrity and confidentiality using blockchain-based internet of things (IoT) architecture

Small-scale third-party accessories vendors face numerous challenges such as (i) warranty period covered for sensitive product, (ii) malicious induction, and (iii) extract root cause of product failure after customer experienced in the real time usage. Therefore, an advanced industrial environment is necessary to resolve these limitations. It offers strong security levels as well as intelligent root cause detection over failure. In this work, propose a blockchain-based internet of things (IoT) architecture for industry 4.0, which includes a voting decision mechanism for IoT device authentication and fault classification based on device and product trust values, respectively. These values are regularly updated in the historical trust value and machinery database for every successful product transaction and identify a new failure cause to set feature for next iteration set from the defective industrial cloud database. The analytical model and simulation results for correct authentication ensure that the proposed architecture achieves 86% eligibility of accuracy across a high ratio of malicious attacks in less converged time.

Dense agent-based HPC simulation of cell physics and signaling with real-time user interactions

IntroductionDistributed simulations of complex systems to date have focused on scalability and correctness rather than interactive visualization. Interactive visual simulations have particular advantages for exploring emergent behaviors of complex systems. Interpretation of simulations of complex systems such as cancer cell tumors is a challenge and can be greatly assisted by using “built-in” real-time user interaction and subsequent visualization.MethodsWe explore this approach using a multi-scale model which couples a cell physics model with a cell signaling model. This paper presents a novel communication protocol for real-time user interaction and visualization with a large-scale distributed simulation with minimal impact on performance. Specifically, we explore how optimistic synchronization can be used to enable real-time user interaction and visualization in a densely packed parallel agent-based simulation, whilst maintaining scalability and determinism. We also describe the software framework created and the distribution strategy for the models utilized. The key features of the High-Performance Computing (HPC) simulation that were evaluated are scalability, deterministic verification, speed of real-time user interactions, and deadlock avoidance.ResultsWe use two commodity HPC systems, ARCHER (118,080 CPU cores) and ARCHER2 (750,080 CPU cores), where we simulate up to 256 million agents (one million cells) using up to 21,953 computational cores and record a response time overhead of ≃350 ms from the issued user events.DiscussionThe approach is viable and can be used to underpin transformative technologies offering immersive simulations such as Digital Twins. The framework explained in this paper is not limited to the models used and can be adapted to systems biology models that use similar standards (physics models using agent-based interactions, and signaling pathways using SBML) and other interactive distributed simulations.

Control strategy for dynamic operation of multiple chillers under random load constraints

Under the influence of building load stochasticity and thermal inertia of central air conditioning system, the lag of control strategy makes it difficult for the cooling source system to meet the cooling demand of users in real time, which affects the operational energy efficiency ratio of the cooling source system. In this regard, this paper proposes a chiller dynamic optimization method based on Markov chain model predictive control algorithm, which studies the optimization of parallel operation characteristics of multiple chillers on the basis of correcting the occurrence of small probability events and avoiding frequent chiller starts and stops, reveals the prediction law of hourly load distribution of the future air conditioning system, explores the balance point between the prediction time of building load and the calculation time of chiller control strategy, provides a theoretical basis and reference for determining the time limit of the optimal calculation and the output of the optimal control strategy. The study provides a theoretical basis and reference for determining the time limit of the optimal calculation and the output of the optimal control strategy. It is found that the optimal control strategy not only has good optimization effect, but also the confidence is significantly increased by 56.7%.

Research on Intelligent Refrigerator Control based on Artificial Intelligence Algorithm

Artificial intelligence (AI) brings the Internet of Things (IoT) into the construction and development system, which catalyzes the improvement of the construction quality and level of IoT, and efficiently promotes its development in the application process. Now, based on IoT, objects can be connected with the Internet for communication, so as to realize intelligent remote control and management of objects. Therefore, IoT has become an important part of the new generation of information technology. Traditional refrigerator enterprises urgently need to upgrade their products to break through the bottleneck of market development, in which intelligent refrigerators are an important direction. With the advent of the Internet era, the overall level of equipment application and the level of efficient and high-quality information processing have gradually become the new standard of work in the IoT era. In this paper, the control strategy of intelligent refrigerator based on AI is proposed. The intelligent refrigerator can be remotely controlled by computer or mobile phone through the network and the information can be sent to users in real time, which can realize the IoT control of intelligent refrigerator.

Adding Chatbots to Web Applications: Using ASP.NET Core and Angular

The integration of chatbots into web applications has become a prominent trend in enhancing user experience, streamlining processes, and providing immediate customer support. This paper explores the development of chatbots within web applications using ASP.NET Core and Angular, two powerful frameworks for building scalable and maintainable software solutions. By leveraging the strengths of ASP.NET Core for backend services and Angular for the frontend, developers can create sophisticated chatbots that seamlessly interact with users in real time. This paper outlines the architectural design, implementation strategies, and key considerations for adding chatbots to web applications, including natural language processing (NLP) capabilities, user interface design, and deployment best practices. The discussion extends to integrating third-party APIs, handling conversational flows, and ensuring scalability and security in production environments. Through practical examples and case studies, this paper demonstrates how to build a chatbot that not only meets functional requirements but also provides a seamless user experience. The paper concludes with insights into future trends in chatbot development, including AI-driven enhancements and the growing importance of voice-based interactions in web applications.

Geomorphological Heritage in Viñales National Park (Aspiring UNESCO Geopark): Geomatic Tools Applied to Geotourism in Pinar del Río, Cuba

A detailed analysis of the Viñales National Park (VNP) was carried out to evaluate the main geosites of geomorphological interest inside the park. This allowed the selection of areas (AIG) and localities (LIG) with 20 geosites of geological interest (PIG) suitable to evaluate their importance as geoheritage. Moreover, three field trips and one virtual itinerary encompassing the park aimed to improve the geological–geomorphological knowledge to be used in geotouristic and preservation issues. The methodology included preparing five maps: relief, drainage pattern, slopes, lithology and geomorphology (forms and structures) and representing them on a digital terrain model (DTM) for a better visualisation and selecting the geosites (poljes, valleys and mountain fronts). The evaluation of the selected PIG is based on the criteria proposed by Spanish Institute of Geology and Mining, grouping them in places and areas of interest. The virtual itinerary favour real-time usage of web sites and geoportals. As a result, this paper presents map layers and geomorphological descriptions of the AIG and LIG with the most relevant aspects and valuations of the PIG, AIG and LIG. The PIG reached values between 1050 (Viñales Valley) and 365 (La Jutia Valley). The AIG and LIG ranged between 2190 (Santo Tomás Polje) and 675 (La Cuevita Polje).

A mixed reality system combining augmented reality, 3D bio-printed physical environments and inertial measurement unit sensors for task planning

Successful surgical operations are characterized by preplanning routines to be executed during actual surgical operations. To achieve this, surgeons rely on the experience acquired from the use of cadavers, enabling technologies like virtual reality (VR) and clinical years of practice. However, cadavers, having no dynamism and realism as they lack blood, can exhibit limited tissue degradation and shrinkage, while current VR systems do not provide amplified haptic feedback. This can impact surgical training increasing the likelihood of medical errors. This work proposes a novel Mixed Reality Combination System (MRCS) that pairs Augmented Reality (AR) technology and an inertial measurement unit (IMU) sensor with 3D printed, collagen-based specimens that can enhance task performance like planning and execution. To achieve this, the MRCS charts out a path prior to a user task execution based on a visual, physical, and dynamic environment on the state of a target object by utilizing surgeon-created virtual imagery that, when projected onto a 3D printed biospecimen as AR, reacts visually to user input on its actual physical state. This allows a real-time user reaction of the MRCS by displaying new multi-sensory virtual states of an object prior to performing on the actual physical state of that same object enabling effective task planning. Tracked user actions using an integrated 9-Degree of Freedom IMU demonstrate task execution This demonstrates that a user, with limited knowledge of specific anatomy, can, under guidance, execute a preplanned task. In addition, to surgical planning, this system can be generally applied in areas such as construction, maintenance, and education.

Latency Minimization for Mobile Edge Computing Enhanced Proximity Detection in Road Networks

In road networks, mobile users (including vehicles and pedestrians) need to know the proximity relationship with other users in real time, referred to as the problem of proximity detection which is very significant for autonomous driving. However, due to limited computing and storage resources of mobile users and real-time changes of road network status, it becomes a difficult task to calculate and update the proximity relationship between users in real time. Therefore, in this paper, we first propose a computation offloading scheme and a dynamic road network state update model for proximity detection in dynamic road networks based on Mobile Edge Computing (MEC), and formulate the latency optimization problem for proximity detection in the dynamic road network as a nonlinear optimization problem. Then we use the Sequential Least Squares Programming (SLSQP) algorithm to solve the latency optimization problem. In addition, to reduce the running time, we also use the deep reinforcement learning approach, i.e., the Deep Deterministic Policy Gradient (DDPG) algorithm, to address the latency optimization problem. Simulation results show that, compared with the SLSQP algorithm, the DDPG algorithm can effectively and efficiently reduce the computational time of the optimal latency each time by continuously adjusting the task allocation strategy, and the computational time of the DDPG algorithm is two orders of magnitude lower than the SLSQP algorithm.

Interactive Analog Layout Editing With Instant Placement and Routing Legalization

Analog layout design is still primarily reliant on manual efforts. Current fully automated workflows are unable to meet the expectations for flexible customization and are incompatible with existing manual workflows. For both performance and productivity, interactive layout editing has the ability to bridge the gap between manual and automated flows. We present an interactive layout editing system in this study that includes well-defined commands for both placement and routing customization. This is a pioneering work that provides a holistic study on the interactive design methodology for analog layouts and its capability of speeding up design closure. Our framework comes up with the instant placement legalization and routing adjustment mechanism for rapid layout update and modification. The framework is capable of handling real-time user interaction and improving the performance of fully automated layout generators verified by post-layout simulation on real-world analog designs. Experimental results demonstrate the performance enhancement on real-world analog designs with only a few editing commands. As examples, on the low-dropout regulator, our framework can reduce the overshot down and up voltage to nearly <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1/3$ </tex-math></inline-formula> of layout generated by automation tool with two editing commands, and on the operational transconductance amplifier, it achieves 33.5% better common mode rejection ratio with only one command.

Closed-loop network of skin-interfaced wireless devices for quantifying vocal fatigue and providing user feedback

Vocal fatigue is a measurable form of performance fatigue resulting from overuse of the voice and is characterized by negative vocal adaptation. Vocal dose refers to cumulative exposure of the vocal fold tissue to vibration. Professionals with high vocal demands, such as singers and teachers, are especially prone to vocal fatigue. Failure to adjust habits can lead to compensatory lapses in vocal technique and an increased risk of vocal fold injury. Quantifying and recording vocal dose to inform individuals about potential overuse is an important step toward mitigating vocal fatigue. Previous work establishes vocal dosimetry methods, that is, processes to quantify vocal fold vibration dose but with bulky, wired devices that are not amenable to continuous use during natural daily activities; these previously reported systems also provide limited mechanisms for real-time user feedback. This study introduces a soft, wireless, skin-conformal technology that gently mounts on the upper chest to capture vibratory responses associated with vocalization in a manner that is immune to ambient noises. Pairing with a separate, wirelessly linked device supports haptic feedback to the user based on quantitative thresholds in vocal usage. A machine learning-based approach enables precise vocal dosimetry from the recorded data, to support personalized, real-time quantitation and feedback. These systems have strong potential to guide healthy behaviors in vocal use.

Development of a Methane Detection System by a Household Gas Detector

Software has been developed for the correct operation of the air monitoring sensor, which allows real-time information on the concentration of the detected gas in the air. Implemented the ability to notify the user in real time by means of a temporary email address server. The gas detector model presented in this paper has the following advantages: strength; ease of use, which allows it to be used by a wide range of users; low cost of individual components and the model as a whole; portability, allowing installation in small rooms, ventilation shafts, technical openings or ceilings.