Status Of System Research Articles

A Resilient Event‐Triggered Control Scheme for Nonlinear Multi‐Agent Systems Under Communication Delays and Denial‐Of‐Service Attacks

ABSTRACTThis paper investigates the resilient event‐triggered control (ETC) for nonlinear multi‐agent systems in the presence of communication delays and denial‐of‐service (DoS) attacks. The communication delays and asynchronous DoS attack are considered for each communication link. Based on sampling‐receiving events and DoS attack events, flow and jump sets are formulated to describe the system's transmission time status. An independent event‐triggering mechanism (ETM) is designed for each communication link. Furthermore, a resilient control scheme is constructed in which sufficient conditions for achieving system input‐output stability are provided, including the constraints of frequency and DoS attack duration, the conditions of Lyapunov functions, and the maximally allowable transmission interval. Finally, the attitude cooperative control problem of multiple unmanned aerial vehicles (multi‐UAVs) is adopted as a simulation example. The simulation results show the effectiveness of the proposed scheme.

Design and Implementation of An Energy-Efficient Extension Board with Integrated Overcurrent Protection for Home Appliances

Electrical extension boards are a major cause of electrical fires in homes, university hostels, offices, and other key applications worldwide, often due to overloading beyond their rated capacity. Additionally, users frequently forget to turn off appliances like televisions, portable fans, or electric irons, resulting in unnecessary energy waste. These Extension boards, commonly used to power multiple electrical appliances, are widespread in homes and universities, but overloading them can lead to overheating, burning, and potential fire hazards. This is often due to exceeding the board’s rated capacity, which reduces load resistance and increases current flow, particularly in parallel connected domestic loads. To address this risk, this paper presents an innovative energy efficient extension board equipped with overcurrent protection. The system continuously monitors the current drawn by connected appliances and automatically isolates them via a relay mechanism when the load exceeds the rated capacity of 13 amperes. The overcurrent status is displayed on an LCD. A microcontroller, interfaced with a current sensor and relay, manages the process, ensuring the safety of the extension boards and home appliances. Additionally, a buzzer notifies the user of the system's status. Furthermore, the system features a timer function to manage energy consumption based on user defined settings. Both simulation and experimental prototype were employed to verify the design, and the results showed a strong correlation between the expected and actual performance, demonstrating the practicality of the proposed system.

Power modeling of degraded PV systems: Case studies using a dynamically updated physical model (PV-Pro)

Power modeling, widely applied for health monitoring and power prediction, is crucial for the efficiency and reliability of Photovoltaic (PV) systems. The most common approach for power modeling uses a physical equivalent circuit model, with the core challenge being the estimation of model parameters. Traditional parameter estimation either relies on datasheet information, which does not reflect the system's current health status, especially for degraded PV systems, or requires additional I-V characterization, which is generally unavailable for large-scale PV systems. Thus, we build upon our previously developed tool, PV-Pro (originally proposed for degradation analysis), to enhance its application for power modeling of degraded PV systems. PV-Pro extracts model parameters from production data without requiring I-V characterization. This dynamic model, periodically updated, can closely capture the actual degradation status, enabling precise power modeling. PV-Pro is compared with popular power modeling techniques, including persistence, nominal physical, and various machine learning models. The results indicate that PV-Pro achieves outstanding power modeling performance, with an average nMAE of 1.4 % across four field-degraded PV systems, reducing error by 17.6 % compared to the best alternative technique. Furthermore, PV-Pro demonstrates robustness across different seasons and severities of degradation. The tool is available as a Python package at https://github.com/DuraMAT/pvpro.

Research on portable airway impedance monitoring device based on expiratory oscillation

Monitoring airway impedance has significant clinical value in accurately assessing and diagnosing pulmonary function diseases at an early stage. To address the issue of large oscillator size and high power consumption in current pulmonary function devices, this study adopts a new strategy of expiration-driven oscillation. A lightweight and low-power airway impedance monitoring system with integrated sensing, control circuitry, and dynamic feedback system, providing visual feedback on the system's status, was developed. The respiratory impedance measurement experiments and statistical comparisons indicated that the system could achieve stable measurement of airway impedance at 5 Hz. The frequency spectrum curves of respiratory impedance ( R and X) showed consistent trends with those obtained from the clinical pulmonary function instrument, specifically the impulse oscillometry system (IOS). The differences between them were all less than 1.1 cm H 2O·s/L. Additionally, there was a significant statistical difference in the respiratory impedance R5 between the exercise and rest groups, which suggests that the system can measure the variability of airway resistance parameters during exercise. Therefore, the impedance monitoring system developed in this study supports subjects in performing handheld, continuous measurements of dynamic changes in airway impedance over an extended period of time. This research provides a foundation for further developing low-power, portable, and even wearable devices for dynamic monitoring of pulmonary function.

Development of a Low Cost Poultry Egg Incubation System

The egg incubator is a device that allows poultry breeders to generate more chicks without the mother hen. To do this, the system was automated using an Arduino microcontroller. The DHT11 sensor is at the core of the temperature and humidity monitoring system, and the system's status is presented on the LCD at regular intervals. The results reveal that the system was able to automatically maintain the temperature within the prescribed range, allowing the eggs to hatch after 21 days.

Integrated Fault Detection System for Underground Lines Using ESP32 and LORA Communication Using IOT

Abstract: This paper introduces an advanced fault detection system tailored for underground line infrastructure, featuring the integration of ESP32 microcontroller technology and LORA communication protocols. The primary objective of this system is to enhance the reliability and efficiency of fault detection processes, crucial for maintaining the integrity of underground lines. At the core of this system lies the ESP32 microcontroller, serving as the central processing unit responsible for coordinating various functionalities. The detection mechanism incorporates six toggle switches strategically positioned along the underground lines, each assigned to monitor a specific location segment. In the event of a fault occurrence, the corresponding toggle switch is triggered, promptly initiating the fault detection process. Upon fault detection, the system seamlessly orchestrates the transmission of GPS location data to a LORA communication device. This data transmission mechanism ensures rapid dissemination of critical information regarding the fault's precise location. Additionally, an LED indicator is employed to provide immediate visual feedback, enabling quick identification of fault occurrences. Moreover, a LCD display unit offers realtime updates on the system's operational status, facilitating efficient monitoring and management. On the receiving end, the LORA communication device securely receives the transmitted data and forwards it to the cloud infrastructure using IOT technology. This cloud integration enables seamless data aggregation, storage, and analysis, empowering stakeholders with valuable insights into underground line health and performance.

A bi-objective model for location, dispatch and relocation of ambulances with a revision of dispatch policies

PurposeUntimely responses to emergency situations in urban areas contribute to a rising mortality rate and impact society's primary capital. The efficient dispatch and relocation of ambulances pose operational and momentary challenges, necessitating an optimal policy based on the system's real-time status. While previous studies have addressed these concerns, limited attention has been given to the optimal allocation of technicians to respond to emergency situation and minimize overall system costs.Design/methodology/approachIn this paper, a bi-objective mathematical model is proposed to maximize system coverage and enable flexible movement across bases for location, dispatch and relocation of ambulances. Ambulances relocation involves two key decisions: (1) allocating ambulances to bases after completing services and (2) deciding to change the current ambulance location among existing bases to potentially improve response times to future emergencies. The model also considers the varying capabilities of technicians for proper allocation in emergency situations.FindingsThe Augmented Epsilon-Constrained (AEC) method is employed to solve the proposed model for small-sized problem. Due to the NP-Hardness of the model, the NSGA-II and MOPSO metaheuristic algorithms are utilized to obtain efficient solutions for large-sized problems. The findings demonstrate the superiority of the MOPSO algorithm.Practical implicationsThis study can be useful for emergency medical centers and healthcare companies in providing more effective responses to emergency situations by sending technicians and ambulances.Originality/valueIn this study, a two-objective mathematical model is developed for ambulance location and dispatch and solved by using the AEC method as well as the NSGA-II and MOPSO metaheuristic algorithms. The mathematical model encompasses three primary types of decision-making: (1) Allocating ambulances to bases after completing their service, (2) deciding to relocate the current ambulance among existing bases to potentially enhance response times to future emergencies and (3) considering the diverse abilities of technicians for accurate allocation to emergency situations.

An Adaptive Energy Management Approach for Battery-Supercapacitor Hybrid Energy Storage System

Energy storage systems (ESS) are indispensable in daily life and have two types that can offer high energy and high power density. Hybrid energy storage systems (HESS) are obtained by combining two or more energy storage units to benefit both types. Energy management systems (EMS) are essential in ensuring HESS's reliability, high performance, and efficiency. One of the most critical parameters for EMS is the battery state of health (SoH). Continuous monitoring of the SoH provides essential information regarding the system's status, detects unusual performance degradations and enables planned maintenance, prevents system failures, helps keep efficiency at a consistently high level, and helps ensure energy security by reducing downtime. The SoH parameter depends on parameters such as Depth of Discharge (DoD), charge and discharge rate (C-Rate), and temperature. Optimal values of these parameters directly affect the lifetime and operating performance of the battery. The proposed Adaptive Energy Management System (AEMS) uses the SoH parameter of the battery as the control input. It provides optimal control by dynamically updating the C-Rate and DoD parameters. In addition, the supercapacitor integrated into the system with filter-based power separation prevents deep discharge of the batteries. Under the proposed AEMS control, HESS has been observed to generate 6.31% more energy than a system relying solely on batteries. This beneficial relationship between supercapacitors and batteries efficiently managed by AEMS opens new possibilities for advanced energy management in applications ranging from electric vehicles to renewable energy storage systems.

Early prediction of long hospital stay for Intensive Care units readmission patients using medication information

ObjectivePredicting Intensive Care Unit (ICU) Length of Stay (LOS) accurately can improve patient wellness, hospital operations, and the health system's financial status. This study focuses on predicting the prolonged ICU LOS (≥3 days) of the 2nd admission, utilizing short historical data (1st admission only) for early-stage prediction, as well as incorporating medication information. Materials and methodsWe selected 18,572 ICU patients’ records from the MIMIC-IV database for this study. We applied five machine learning classifiers: Logistic regression (LR), Random Forest (RF), Support Vector Machine (SVM), AdaBoost (AB) and XGBoost (XGB). We computed both the sum dose and the average dose for the medication and included them in our model. ResultsThe performance of the RF model demonstrates the highest level of accuracy compared to other models, as indicated by an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.716 and an Expected Calibration Error (ECE) of 0.023. DiscussionThe calibration improved all five classifiers (LR, RF, SVC, AB, XGB) in terms of ECE. The most important two features for RF are the length of 1st admission and the patient's age when they visited the hospital. The most important medication features are Phytonadione and Metoprolol Succinate XL. Also, both the sum and the average dose for the medication features contributed to the prediction task. ConclusionOur model showed the capability to predict the prolonged ICU LOS of the 2nd admission by utilizing the demographic, diagnosis, and medication information from the 1st admission. This method can potentially support the prevention of patient complications and enhance resource allocation in hospitals.

Intelligent Aeroponics System Using Machine Learning

The Intelligent Aeroponics System Using Machine Learning is a cutting-edge agricultural technology that leverages the power of artificial intelligence to enhance the cultivation of plants in a soilless environment. This system integrates an array of sensors to collect data on crucial environmental parameters, plant health, and growth conditions. A machine learning model, trained on historical data, analyzes this information to make informed decisions in real-time. The system employs data- driven algorithms to optimize the delivery of nutrients, misting intervals, lighting conditions, and environmental control parameters. It includes a user-friendly interface for remote monitoring and control, enabling users to receive timely alerts and notifications about the system's status. Additionally, computer vision-based models are used to detect early signs of pests and diseases, allowing for swift intervention. With an emphasis on energy efficiency, security, and scalability, this intelligent aeroponics system offers a sustainable approach to crop cultivation. Its continuous feedback loop and data analysis ensure crop optimization and resource conservation. The system can be adapted to various plant types and growth stages, making it a valuable tool for modern agriculture. Overall, the Intelligent Aeroponics System Using Machine Learning not only maximizes crop yields but also minimizes resource consumption, enhancing the efficiency and sustainability of agricultural practices. Keywords: aeroponic, monitoring system, automated system, Arduino, IoT, machine learning, lettuce.

디지털 적층 제조 장비의 사용자 중심 HMI 디자인 가이드 제안

With the advancement of metal additive manufacturing technology and the digitization of equipment, the need for user-centered design in HMI (Human-Machine Interface) has become increasingly apparent. However, research on design guides and usability test for industrial equipment has been lacking. Therefore, this study applied user-centered design principles to design and manufacture a prototype HMI for metal additive manufacturing equipment, followed by a usability test of the HMI prototype. Based on usability testing and in-depth interview analysis, this study proposes UX/UI design guidelines for user-centered industrial equipment HMI. The usability evaluation highlighted the need to improve the visibility of the system's real-time status, enhance interface intuitiveness for error prevention, and address emergency situations. It is crucial that the interface reflects both the visible and invisible states of the equipment, considering that the operation targets are mechanical equipment but the operating devices are HMI screens. Furthermore, for industrial equipment with numerous complex functions, it is essential to restrict or guide user actions through permission settings. Through the industrial PBF (Powder Bed Fusion) method metal additive manufacturing equipment HMI design guides proposed by this study, we anticipate the development of user-friendly and productivity-enhancing HMI designs.

Manipulating dissolved oxygen concentration to control the partitioning between carbon oxidation and redirection in the A-stage high rate activated sludge process

The adsorption/bio-oxidation (A/B) process has emerged as a remedy to the significant energy use from wastewater treatment plants. In such a process, the A-stage's objective is to balance maintaining high COD redirection towards recovery streams with achieving high effluent quality to facilitate energy savings in the B-stage. While SRT was intensively investigated to control A-stage performance, this study investigates the use of dissolved oxygen (DO) concentration under two adverse conditions: short and long residence times (SRT and HRT). The findings indicate that excessive oxidation is impeded at short residence times, meanwhile, high DO concentration (>2 mgO2/L) is required for enhanced substrate utilization and high removal efficiencies. Conversely, low DO concentration (< 0.5 mgO2/L) is needed at long residence times to control the system's oxidative status and partition more COD towards redirection instead of oxidation. Whereas, high removal efficiency is governed by the long residence times. Notably, this study implies that the low COD removal efficiencies in existing A-stage systems can be referred to the need to deliver oxygen at rates beyond the maximum achievable oxygen transfer rates in full-scale systems. Novelly, this study demonstrates that systems operated at lower rates coupled with low DO concentration can achieve higher COD removal efficiencies while maintaining high COD diversion.

A practical methodology for distributed energy resources (DER) valuation in distribution networks

Under most NEM policies, DER customers sell excess electricity at full retail price. Although this structure helped the widespread growth of DER, it is argued to subsidize DER at the expense of non-DER customers. In that regard, value-based compensation emerged, and as of 2022, at least 12 states have conducted studies to investigate alternative NEM compensation methods to inform new policy decisions. DER can provide various benefits to the distribution system, including energy, reliability, resilience, environmental, societal, and investment deferral. Each benefit has an individual value, and calculating these values is challenging. This paper presents a practical methodology to calculate the DER location value. The proposed approach utilizes established utility planning and reliability criteria. It analyzes the distribution system's current status, capturing any planning or reliability criteria violations, and determines the utility upgrade investment costs to remediate them. The proposed approach then determines the size, locational, and temporal profile of the DER that can be added to the distribution network and defer any of the utility upgrade investment costs, thus obtaining the value to the system and assigning a monetary value. The results provide a nodal distribution value of DER to the distribution system.

An analysis of Nepal's federal health system in the context of the COVID-19 pandemic and the Sustainable Development Goals

Nepal's public health slogan was 'Health for All' with integrated community health services in 1991. The female community health volunteer network was created. District Public health offices (DPHO) managed these programs in their districts. After 2008 federalism, 2017 brought federal, provincial, and local governments. The federal health system substituted the administrative District Public / Health Offices in districts. The local governments or health institutions now oversee the Sustainable Development Goals (SDGs). The federal and provincial authorities should have employed qualified professionals to oversee public health at local health facilities. Healthcare efficacy and goal attainment are concerns today. This study did a microscopic investigation using health system studies. The researcher conducted field observations and stakeholder interviews and participated in public discussions. Data from Nepal's federal health system's community status was compared using SWOT analysis. The study delved into specific research gaps thoroughly. According to previous research, health infrastructure could be more effective with financial expenditures. A hypothesis was formed after comparing further information to domestic and international situations. The researcher emphasizes the need for good governance and suggests allocating 6% of national GDP to health spending, in line with WHO recommendations. To fulfil the needs of the healthcare sector, deliver quality medical services, and attain SDGs, it is imperative to ensure the presence of adequately trained health professionals at the local government level. The head of the local health institution must conduct administrative work connected to public health. Hence, public health-trained health staff are needed there.

Typology of planktonic food webs and associated emerging properties as indicators of the ecological status of a permanently disturbed Gulf of Gabès

This study highlights the importance of coupling the typology of planktonic food webs and their emerging properties to better describe the ecological status of an ecosystem under permanent disturbance mainly caused by phosphate industry. Linear inverse models were built to describe four stations under various levels of nutrient pressure, using the Markov Chain Monte Carlo method to estimate known and unknown carbon flows, later used to calculate food web typology ratios. Ecological network analysis (ENA) was used to describe the structural and functional properties of each food web. Based on the food web typology ratios, three planktonic trophic pathways (PTP) with different functional indices were distinguished according to nutrient stress. The microbial food web dominated in the least nutrient-rich environment. It mainly relied on phytoplankton production (picophytoplankton <2 μm) that was mainly transferred by the high microbivory of protozooplankton. In contrast, the herbivorous food web developed in the most nutrient-rich environment, where biogenic carbon was mainly produced by large phytoplankton (microphytoplankton >10 μm) and channeled to higher trophic levels by herbivorous protozooplankton and metazooplankton. In the other two stations – moderately nutrient-rich systems – the PTP acted as a multivorous food web. Phytoplankton (small and large size fractions) and non-living components (detritus and dissolved organic carbon) played a significant role in carbon production, and competed with protozooplankton and metazooplankton for its transport. ENA indices revealed that the herbivorous food web, with the highest total system throughput and lowest relative Ascendency and cycling, was the most active but the least organized and stable system. In contrast, the microbial food web, with the lowest total system flux and highest Ascendency, was least active but more organized than the herbivorous food web. The multivorous food web displayed the most recycling and most organized system, with high values of the detritivory-to-herbivory ratio, cycling and Ascendency. In addition to ENA indices, which are considered effective tools for studying the structural and functional properties of food webs, marine ecosystem management efforts heavily focus on using the “marine food web” as a descriptor of the system's ecological status. However, we suggest that the combination of food web typology and ecological indices could be used as an effective tool for the management and assessment of ecosystem health wherever possible, as well as for the study of anthropogenic pressures.

Study on smart grid fault detection based on ZigBee and MapX technologies

As the complexity of the power system and the increasing demand for electricity continue to rise, ensuring the reliability and stability of the power system becomes critically important. This study utilizes ZigBee communication modules to establish a sensor network for real-time monitoring of the power system's status and employs MapX technology to achieve geographic information visualization of the power system. Through an improved RBF-PCA-WFCM algorithm, grid data can be dimensionally reduced, enabling simulation training tailored to grid loads, resulting in more accurate algorithm analysis and optimization. This paper introduces fault localization and diagnostic methods, as well as clustering optimization algorithms, to achieve fault monitoring, analysis, and identification in the smart power grid. Research findings demonstrate that the intelligent power grid data analysis and fault detection system based on ZigBee and MapX technologies can enhance the reliability of the power system and better support the increasing demands of the evolving field of electricity.

Remote Monitoring, Control and Data Visualization for a Solar Water Pumping System

Access to clean water is a significant challenge in many regions, including Sukkur, Pakistan. The effective management of water resources is a critical challenge, particularly in areas with limited access to surface water sources. This paper presents a remote monitoring of water pumping system designed to address water distribution challenges in Sukkur, Pakistan. The system utilizes a combination of hardware components, including Arduino Uno, Raspberry Pi 2, ultrasonic sensor, and GSM modules, to enable remote monitoring, control, and data visualization. The system architecture incorporates Node-RED, a powerful flow-based programming tool, to facilitate data communication, storage, and visualization. To enable remote monitoring and control, users can send SMS commands to the Arduino Uno, connected to the GSM module, to query the system's status and control the pump's operation. Additionally, a mobile application developed using the MIT App Inventor platform allows users to interact with the system, visualize real-time and historical data, and receive updates on water levels and pump status. The Raspberry Pi 2 serves as a server and cloud storage for the system.

Handling complete short-term data logging failure in smart buildings: Machine learning based forecasting pipelines with sliding-window training scheme

This paper implements a machine learning(ML)-based procedure for constructing the missing sensor(s) data in a net zero energy building in case of complete failure in data recording (for up to one hour). In the first scenario, missing temperature data is re-created using the sensor's ex-ante data, the HVAC system's status flag, and the ambient conditions. In the second scenario, the temperature data (until failure occurred) from two close-by spaces are also utilized as inputs. For each scenario, ML-based pipelines' performance is first assessed by considering different prediction horizons using a benchmark algorithm. Next, each pipeline's most promising features and the most suitable algorithm are identified. Using the obtained optimal pipeline, a sliding window-based training scheme is implemented, and the size of the training window is optimized. It is shown that feature selection, algorithm optimization procedures, and the sliding window-based training scheme notably improve the forecasting performance. The proposed methodology can be deployed as a tool in intervals with total data logging failure, providing data to ML-based controllers in smart buildings and avoiding disruptions in the building management system.

Demand response based microgrid's economic dispatch

The development of energy management tools for next-generation Distributed Energy Resources (DER) based power plants, such as photovoltaic, energy storage units, and wind, helps power systems be more flexible. Microgrids are entities that coordinate DERs in a persistently more decentralized fashion, hence decreasing the operational burden on the main grid and permitting them to give their full benefits. A new power framework has emerged due to the integration of DERs-based microgrids into the conventional power system. With the rapid advancement of microgrid technology, more emphasis has been placed on maintaining the microgrids' long-term economic feasibility while ensuring security and stability. The objective of this research is to provide a multi-objective economic operation technique for microgrids containing air-conditioning clusters (ACC) taking demand response into account. A dynamic price mechanism is proposed, accurately reflecting the system's actual operational status. For economic dispatch, flexible loads and air conditioners are considered demand response resources. Then, a consumer-profit model and an AC operating cost model are developed, with a set of pragmatic constraints of consumer comfort. The generation model is then designed to reduce the generation cost. Finally, a microgrid simulation platform is developed in MATLAB/Simulink, and a case is designed to evaluate the proposed method's performance. The findings show that consumer profit increases by 69.2% while ACC operational costs decrease by 18.2%. Moreover, generation costs are reduced without sacrificing customer satisfaction.

Sewage Monitoring and Maintenance Alert using IoT

bstract: We're planning to create a smart gadget that can detect when the drainage system becomes clogged, and if water overflows as a result of this clogging, we'll notify the local authority so they can take the appropriate measures. Additionally, the Android application allows for real-time monitoring of the drainage system's status. Four sensors will be used as input devices for the project: a pH sensor to measure the acidity of the sewage water as accurately as possible so that the water can be treated in a wastewater treatment plant and can be reused; an ultrasonic sensor to detect blockages; a water level/float sensor to detect overflows of water; and a gas sensor to measure the to detect all toxic gases released from the sewage. Finally, by inputting the picture samples collected during sewage monitoring, we may use image processing to find fractures in the pipes. The ESP8266, which serves as both a microcontroller and a wireless module, receives and processes the data from these sensors. Here, a push notice is given to the mobile device in case of Blockage or Overflow in addition to the real-time ultrasonic distance being supplied to the Blynk/Adafruit Application. Using our smartphones, we can control any IoT device using the Blynk/Adafruit application, which can operate on Android, iOS, and Windows platforms. To develop the graphical user interface for our IoT application, we may create our own GUI