Performance Monitoring System Research Articles

Employee Attendance and Performance Monitoring System with Facial Recognition Technology and Data Analytics

Paper-based attendance monitoring is being altered daily, whether in public or private, making it unreliable in evaluating the authenticity of attendance. In a career context, attendance plays a fundamental role, and traditional methods for monitoring it need to be updated. The emergence of biometric identification, particularly facial recognition, offers a promising solution. This study introduces FRaDA, an employee data Analytics with a Face Recognition System developed explicitly for Talisay Elementary School (TES) faculty and staff. The system aims to digitize attendance monitoring and employee evaluation processes by leveraging facial recognition technology. It captures and records attendance via live camera feed, generates efficient performance and attendance reports, minimizes recording errors and tampering, automates Daily Time Records (DTR) generation, and facilitates intensive attendance management. The methodological approach follows the Agile Scrum model and involves Initialization, Planning and Estimating, Implementation, Review, and Releasing phases, ensuring a systematic and efficient development process. Key highlights include the evaluation process with an overall score of 3.00, indicating the project's feasibility and excellence. In conclusion, Project FRaDA offers a practical and efficient solution for attendance management and employee evaluation, addressing the limitations of traditional methods and meeting the institution's needs.

Design and Long-Term Performance of a Pilot Wastewater Heat Recovery System in a Commercial Kitchen in the Tourism Sector

This paper assesses the performance of waste heat recovery from commercial kitchen wastewater in practice. A pilot study of heat recovery from the kitchen at Penrhyn Castle, a tourist attraction in North Wales (UK), is outlined. The pilot heat recovery site was designed and installed, comprising a heat exchanger, recirculation pumps, buffer tank and an extensive temperature/flow monitoring system for performance monitoring of the waste heat recovery system. Continuous monitoring was conducted for a period of 8 months, covering the 2022 tourist season. The recovered heat from the kitchen wastewater preheats the incoming cold freshwater supply and consequently reduces the amount of energy consumed for subsequent water heating. Retrofitting the pilot heat recovery system to the kitchen drains resulted in a heat saving of 240 kWh per month on average, a reduction of 928.8 kg CO2e per year, and a payback period for the investment costs of approximately two years, depending on the cost of energy supply. The presented results illustrate the potential of this form of renewable heat in reducing the carbon footprint of water heating activities in buildings and the hospitality sector.

Digital Twin-Driven Framework for TBM Performance Prediction, Visualization, and Monitoring through Machine Learning

The rapid development in underground infrastructure is encouraging faster and more modern ways, such as TBM tunneling, to meet the needs of the world. However, tunneling activities generate complex and heterogeneous data, which makes it difficult to visualize the performance of a project. Advancements in information technology, such as digital twins and machine learning, provide platforms for digital demonstration, visualization, and system performance monitoring of such data. Therefore, this study proposes a digital twin-driven framework for TBM performance prediction through machine learning, visualization, and monitoring. This novel approach integrates machine learning and real-time performance data to predict, visualize, and monitor the status of the tunnel construction progress. A digital twin virtual model of TBM was constructed based on TBM design parameters, the input parameter, boring energy, RPM, torque, thrust force, speed, gripper pressure, total revolution, and Q-value provided to SVR and ANN models to predict the TBM AR and PR, and TBM daily progress was visualized continuously. The predictive performance indices R2 (0.97) and RMSE (0.011) were estimated for AR prediction, showing the accuracy of the proposed model. To demonstrate the proposed framework, this study shows the its effectiveness. By implementing this framework, stakeholders can minimize the risk associated with the cost and schedule of a tunneling project by simultaneously visualizing and monitoring the performance of TBMs through digital twin and machine learning algorithms.

Sistem Monitoring dan Evaluasi Kinerja Dosen Menggunakan Metode Multi Factor Evaluation Process

Monitoring and evaluating the performance of lecturers at the Faculty of Engineering, Tamansiswa Bachelor Wiyata University, the system that has been running has experienced many problems with the increasing number of active students and limited human resources. The evaluation of the learning questionnaire is filled out through the Google Form and the lecturer's data performance is processed using software such as Microsoft Office Excel or the like. In this study the aim was to produce a system that could support learning monitoring and evaluation activities in measuring the level of lecturer performance. The system is needed to process data as a tool to input and report on monitoring and evaluating lecturer performance. The method applied is the Multi Factor Evaluation Process (MFEP) using four criteria, namely pedagogic, professional, personality and social competence. Based on the results of the tests that have been carried out, the lecturer performance monitoring and evaluation system can provide decisions in the form of lecturer performance levels. The system can be used at several user levels, namely faculty academics, heads of study programs and Gugus Mutu Prodi (GMP) in assessing or measuring lecturer performance.

Enabling Grant-Free URLLC for AoI Minimization in RAN-Coordinated 5G Health Monitoring System

Age of information (AoI) is used to evaluate the performance of 5G health monitoring systems because stale data can be fatal for patients with serious illness. Recently, grant-free ultra-reliable and low latency communications (URLLC) have shown greater potential of minimizing AoI than conventional grant-based approaches; however, existing grant-free schedulers cannot provide guaranteed performance in 5G health monitoring systems because they involve two fundamental problems in time and frequency domains, namely the joint scheduling problem and physical resource block (PRB) allocation. In this study, we investigate two resource allocation problems for the first time, aiming to enable grant-free URLLC to minimize AoI in 5G health monitoring systems. Specifically, we propose two adaptive solutions based on an open radio access network-coordinated wireless system: 1) a joint scheduling algorithm and 2) an adaptive PRB allocation algorithm. To verify the effectiveness of the proposed solutions, we built a simulation environment similar to a real health monitoring system and captured the performance variations under realistic deployment scenarios.

Retracted: Operational Status Monitoring and Fault Diagnosis System of Transformer Equipment

Retracted: Operational Status Monitoring and Fault Diagnosis System of Transformer Equipment

Experimental performance of soil monitoring system using IoT technique for automatic drip irrigation

SummaryThis paper proposes an IoT‐enabled soil monitoring system using wireless sensor network for automatic irrigation in agricultural applications, especially for lemongrass plants, where an automated control system is required for irrigation applications. This can solve the problem of the water crisis, which is faced by the farmers during the cultivation of the crop in the field. This controls the water supply in the irrigation process using an IoT communication system. A system architecture for soil monitoring and controlling irrigation using IoT technique is designed where the different sensors and actuators like humidity, soil moisture, temperature, pump, and so forth are connected with a node microcontrol unit and message queuing telemetry transport (MQTT) protocol for enhancing communication capabilities. This wireless sensor network gives feedback to the system. This provides automation by the on/off pump system during drip irrigation. The sensor data are displayed on a PC or mobile phone through wireless communication and an IoT cloud platform. An experimental testing setup is developed and the experimental performance of a soil monitoring system using IoT technique for automatic drip irrigation has been carried out and soil moisture data are also stored in a cloud server for analytics. The performance shows that the MQTT protocol sends data within 48 s to the IoT cloud so that the data can be acquired in a faster manner. This shows that this kind of soil monitoring system is suitable for automatic drip irrigation, which enhances the farming process and overcomes the water crises in the agricultural system by reducing the wastage of water.

THE IMPACT OF BUSINESS INTELLIGENCE SYSTEMS ON PROJECT MANAGEMENT IN HIGHER EDUCATION: AN EMPIRICAL STUDY

The successful completion of projects depends on effective project management, particularly in the complex and dynamic environment of higher education institutions. Business intelligence (BI) tools can assist identify problems and hazards, facilitate decision-making, and offer insightful information on how projects are performing. The purpose of this study is to examine how BI systems are used for project management in higher education institutions and to suggest a successful project management strategy that incorporates BI systems. To determine the present state-of-the-art in project management and BI systems in higher education, the research technique includes a rigorous literature review of academic and industry sources. An analysis of a case study from a university that has used a BI system for project management is also included in the Study. The study's conclusions imply that BI systems can enhance project management at higher education institutions by supplying precise real-time data on project performance, risks, and problems. By facilitating better decision-making, early issue detection, and prompt problem resolution, the deployment of a BI system has improved project management, according to the case study analysis. The study suggests a successful project management strategy that incorporates BI systems in higher education institutions based on the findings. The following steps make up the suggested strategy: Project objectives and scope definition, project planning, BI system implementation, BI system performance monitoring, project issue and risk identification and mitigation, and continuous improvement are the steps in a project. The suggested framework can assist institutes of higher learning in managing projects successfully by making use of BI tools. The paper also outlines the critical success criteria for BI system deployment in project management in higher education institutions, such as top management support, transparent communication, user training, and efficient data governance.

Development of a Simplified Performance Monitoring System for Small and Medium Sized Ships

Development of a Simplified Performance Monitoring System for Small and Medium Sized Ships

Midfrontal mechanisms of performance monitoring continuously adapt to incoming information during outcome anticipation

Performance monitoring is essential for successful action execution and previous studies have suggested that frontomedial theta (FMT) activity in scalp-recorded EEG reflects need for control signaling in response to negative outcomes. However, these studies have overlooked the fact that anticipating the most probable outcome is often possible. To optimize action execution, it is necessary for the time-critical performance monitoring system to utilize continuously updated information to adjust actions in time. This study used a combination of mobile EEG and virtual reality to investigate how the performance monitoring system adapts to continuously updated information during brief phases of outcome evaluation that follow action execution. In two virtual shooting tasks, participants were either able to observe the projectile and hence anticipate the outcome or not. We found that FMT power increased in response to missing shots in both tasks, but this effect was suppressed when participants were able to anticipate the outcome. Specifically, the suppression was linearly related to the duration of the anticipatory phase. Our results suggest that the performance monitoring system dynamically integrates incoming information to evaluate the most likely outcome of an action as quickly as possible. This dynamic mode of performance monitoring provides significant advantages over idly waiting for an action outcome before getting engaged. Early and adaptive performance monitoring not only helps prevent negative outcomes but also improves overall performance. Our findings highlight the crucial role of dynamic integration of incoming information in the performance monitoring system, providing insights for real-time decision-making and action control.

Optimal edge-cloud collaboration based strategies for minimizing valid latency of railway environment monitoring system

Response speed is vital for the railway environment monitoring system, especially for the sudden-onset disasters. The edge-cloud collaboration scheme is proved efficient to reduce the latency. However, the data characteristics and communication demand of the tasks in the railway environment monitoring system are all different and changeable, and the latency contribution of each task to the system is discrepant. Hence, two valid latency minimization strategies based on the edge-cloud collaboration scheme is developed in this paper. First, the processing resources are allocated to the tasks based on the priorities, and the tasks are processed parallelly with the allocated resources to minimize the system valid latency. Furthermore, considering the differences in the data volume of the tasks, which will induce the waste of the resources for the tasks finished in advance. Thus, the tasks with similar priorities are graded into the same group, and the serial and parallel processing strategies are performed intra-group and inter-group simultaneously. Compared with the other four strategies in four railway monitoring scenarios, the proposed strategies proved latency efficiency to the high-priority tasks, and the system valid latency is reduced synchronously. The performance of the railway environment monitoring system in security and efficiency will be promoted greatly with the proposed scheme and strategies.

Perceptions of Continuous Glucose Monitoring Systems in the T1D Exchange Diabetes Registry: Satisfaction, Concerns, and Areas for Future Improvement.

Manufacturers continue to improve performance and usability of continuous glucose monitoring (CGM) systems. As CGM becomes a standard of care, especially for people on insulin therapy, it is important to routinely gauge how satisfied people with diabetes are with this technology. This article describes survey feedback from a large cohort of people with diabetes using older and current CGM systems and highlights areas of current satisfaction, concern, and future system improvement.

Exploring Digital Twin-Based Fault Monitoring: Challenges and Opportunities.

High efficiency and safety are critical factors in ensuring the optimal performance and reliability of systems and equipment across various industries. Fault monitoring (FM) techniques play a pivotal role in this regard by continuously monitoring system performance and identifying the presence of faults or abnormalities. However, traditional FM methods face limitations in fully capturing the complex interactions within a system and providing real-time monitoring capabilities. To overcome these challenges, Digital Twin (DT) technology has emerged as a promising solution to enhance existing FM practices. By creating a virtual replica or digital copy of a physical equipment or system, DT offers the potential to revolutionize fault monitoring approaches. This paper aims to explore and discuss the diverse range of predictive methods utilized in DT and their implementations in FM across industries. Furthermore, it will showcase successful implementations of DT in FM across a wide array of industries, including manufacturing, energy, transportation, and healthcare. The utilization of DT in FM enables a comprehensive understanding of system behavior and performance by leveraging real-time data, advanced analytics, and machine learning algorithms. By integrating physical and virtual components, DT facilitates the monitoring and prediction of faults, providing valuable insights into the system's health and enabling proactive maintenance and decision making.

Maximum AoI Minimization for Target Monitoring in Battery-Free Wireless Sensor Networks

Age of Information (AoI) has been proposed to measure the freshness of the sensory data for IoT applications. In Battery-free WSNs (BF-WSNs), The AoI minimization data collection problem has been extensively studied. Apart from data collection, target monitoring is also an important application for BF-WSNs. To capture the freshness of the sensory data, the AoI of the sensory data related to targets (The AoI of targets) needs to be considered. To guarantee the performance of the target monitoring system, the maximum AoI of all targets should be minimized. However, existing works mainly investigated the AoI of the sensory data related to nodes (The AoI of nodes). Also, existing energy models are not practical enough for battery-free nodes. To deal with those problems, in this paper, we first propose a more practical energy model. Then the problem of Maximum AoI minimization for Target monitoring in Battery-free WSNs (MTB) is formally defined based on the proposed energy model. A two-stage algorithm is proposed to solve MTB optimally, in which all nodes in the network are scheduled collaboratively to monitor all the targets in the monitoring field. Extensive simulations and real-world experiments verify the high performance of our algorithm and energy model.

Design and Experimental Validation of a Multiband Conformal Patch Antenna for Animal-Ingestible Bolus Applications

Electronic boluses with biotelemetry capabilities enable wireless monitoring of animals’ physiological data (e.g., temperature, pH). The aim of this study was to design and experimentally validate a novel multiband (434, 868, 1400) MHz conformal patch antenna for in-body biotelemetry applications for cows. The optimal frequency band was studied prior to the design of the antenna, based on the dielectric measurements of the antenna environment (i.e., rumen). The antenna was integrated in a 13.5 cm × ø3 cm bolus and simulated in a ø300 mm spherical phantom with electromagnetic properties of cows’ rumen fluid. The proposed antenna presented a high performance with a realized gain of (–38.5; –41.2; –45.7) dBi and a radiation efficiency of (0.012%; 0.0045%; 0.001%) at 434, 868, and 1400 MHz, respectively. Following the numerical analysis and optimization, a prototype was manufactured to experimentally evaluate the antenna performance. Good agreement was obtained between the measurements and simulations both for reflection coefficients and radiation performance. The measured gain was –36.3 dBi, –40.4 dBi, and –43.6 dBi, at 434, 868, and 1400 MHz, respectively. The proposed multiband antenna will enable the development of a new generation of boluses for animal biotelemetry applications to enhance the performance of the animal monitoring systems.

Implementatiton of Evaluation Registration Outpatient Computerized Hospital Using a Web-based Technology Accepted Model System

The Information of health system is combination of devices and procedures used for manage information cycle from data collection to providing feedback infomation, for support the implementation of appropriate actions in planning, implementation and monitoring of health system performance. Outpatient registration is the main service received at an outpatient registration. Technologi Accepted Model System created using the PHP programming language and mysql database. The WEB-based Technology Acceptance Model (TAM) is in the form of all evaluation models that can determine the readiness of officers to accept a system. Application of the Web-based Technology Accepted Model System in Evaluating officer acceptance of the computerized Outpatient Registration information system at a hospital in Boyolali, Central Java, Indonesia. The Technology Accepted Model in this study used 5 variables where the results of the implementation of the Technology Accepted Model System showed that the analysis of customer satisfaction with outpatient application systems on the Perceived ease of use dimension obtained satisfied results of (64.8%), on the Perceived usefulness dimension the results obtained satisfied by (63.3%), on the Attitude toward using dimension obtained satisfactory results of (64%), on the Behavioral intention to use dimension obtained appropriate results of (69%), on the Perceived service availability dimension obtained very satisfied results as large as (69 ,3%). Conclusions from the dimensions of Perceived ease of use, Perceived usefulness, Attitude toward using. Behavioral intention to use, and Perceived service availability indicate that officers are satisfied with the Outpatient Registration Application System at Hospitals

Design of Wireless Sensor Network for Agricultural Greenhouse Based on Improved Zigbee Protocol

Greenhouse cultivation technology has greatly contributed to the development of agriculture in Malaysia. Understanding how to monitor the greenhouse environment with high efficiency and low power consumption is particularly important. In this research, a wireless sensor network for agricultural greenhouses based on the improved Zigbee protocol is designed. Its hardware consists of various sensors and Zigbee nodes commonly used in agricultural greenhouses. On the basis of this hardware, this research designed the network topology of WMN (Wireless mesh network) by comparing the advantages and disadvantages of various topologies, and combined with this structure, proposed an improved ZigBee routing protocol EMP-ZBR to solve the question regarding energy loss and the network congestion of wireless networks. After testing EMP-ZBR and traditional Zigbee routing protocols, the improved EMP-ZBR protocol is superior to traditional Zigbee routing in terms of the end-to-end average delay, packet delivery rate, routing control overhead and routing discovery frequency, which were optimized about 1.1%, 15.2%, 15.2%, 8.1 ms in different mobile pause times, and 9.8%, 19.3%, 15.7% and 121 ms in different packet sending rates. The agreement proves that EMP-ZBR can more effectively alleviate the impact of congestion and improve the overall performance of the data monitoring system for agricultural greenhouses.

Pengaruh Komunikasi Interpersonal, Profesionalitas Dan Kompetensi Terhadap Sistem Monitoring dan Kinerja Paramedis

This study aims to empirically examine the effect of interpersonal communication, professionalism and competence on the monitoring system and paramedic performance. This research was conducted on nurses and midwives who work at the Fatimah Islamic Hospital in Banyuwangi. The research was conducted in April-May 2022. The data collection technique was using a questionnaire. The results showed a positive and significant influence of interpersonal communication on the monitoring system. There is a positive and significant influence of professionalism on the monitoring system. There is a positive and significant influence of competence on the monitoring system. There is a positive and significant influence of interpersonal communication on the performance of nurses. There is a positive but not significant effect of professionalism on the performance of nurses. There is a positive but not significant effect of competence on the performance of nurses. There is a positive and significant effect of the monitoring system on the performance of nurses. There is a positive and significant influence of interpersonal communication on the performance of nurses through the monitoring system. There is a positive and significant effect of professionalism on the performance of nurses through the monitoring system. There is a positive and significant effect of competence on the performance of nurses through the monitoring system.

A comparative study of Chinese women 3 × 3 basketball players exercise load in Tokyo Olympic preparation cycle.

Objective: The aim of this study was to compare the variances in-game loads exhibited by Chinese women's 3 × 3 basketball team across different stages of the preparation cycle for the Tokyo Olympic Games, and to summarize the fundamental regulations governing Chinese women's 3 × 3 basketball training and games, in order to establish a theoretical research foundation for the team's new preparation cycle. Methods: This study measured load-related data during the preparation and main competition periods of the 2019-2021 Tokyo Olympics, from April to August 2019 and from April to June 2021. The aim was to compare the changes and differences in a load of competition during different stages and to explore patterns of load changes during the preparation period. This study used wearable devices authorized by FIFA and NBA, along with the Catapult GPS performance monitoring system from Australia (Catapult&Polar Team) as instruments for collecting sports load data. The OptimEye S5 device was worn around the athlete's neck to collect data prior to the game, while the Open Field™ system was utilized for data editing and report generation post-game. Results: Compared to the primary competition load during the 2019 preparation period, the 2021 preparation period exhibited significant increases and decreases (p < 0.05) in competition load, high-intensity load, the number of explosive moves, the number of high-intensity acceleration, several changes to the left and right, and the number of explosive jumps. During the 2021 preparation period, the mean heart rate, mean heart rate percentage, and mean speed of the race demonstrated significant decreases in comparison to the race during the 2019 preparation period (p < 0.05). Throughout the training period spanning from 2019 to 2021, no significant differences were observed in running distance and maximum speed (p > 0.05). Conclusion: The findings of this study reveal that the national training team has fostered positive adaptive changes in athletes, resulting in a significant enhancement in both load and sports performance science data during competition from 2019 to 2021.

Periodic Transmissions in Random Access Networks: Stressed Period and Delay

Most IoT systems use random access protocols for wireless communication. This paper considers an IoT node that generates periodic traffic to be delivered to a destination over a random access network; each packet of the node is expected to be delivered before its deadline. We say that the node is in a stressed period if, within a time interval, its successive packets miss their deadlines. In many systems, the worst-case performance is significantly affected by stressed periods. Characterizing the stochastic properties of stressed periods is thus of fundamental importance. In this paper, we use a fluid flow model to approximate the evolution of the buffer occupancy (i.e., backlog) at the transmitting node. We derive a relationship between buffer occupancy and delay and formally define a stressed period via a time interval in which the buffer occupancy exceeds a certain threshold. With this model, we analyze the dynamics of the buffer occupancy evolution and obtain the probability distributions of stressed period duration and delay. Real network experiments show that our model can well approximate the distributions of stressed period duration and delay in practical WiFi networks. The theoretical results of this paper can be used to analyze the robustness and worst-case performance of IoT monitoring and control systems built on random access networks.