System Design Research Articles

Integral system safety for machine learning in the public sector: An empirical account

This paper introduces systems theory and system safety concepts to ongoing academic debates about the safety of Machine Learning (ML) systems in the public sector. In particular, we analyze the risk factors of ML systems and their respective institutional context, which impact the ability to control such systems. We use interview data to abductively show what risk factors of such systems are present in public professionals' perceptions and what factors are expected based on systems theory but are missing. Based on the hypothesis that ML systems are best addressed with a systems theory lens, we argue that the missing factors deserve greater attention in ongoing efforts to address ML systems safety. These factors include the explication of safety goals and constraints, the inclusion of systemic factors in system design, the development of safety control structures, and the tendency of ML systems to migrate towards higher risk. Our observations support the hypothesis that ML systems can be best regarded through a systems theory lens. Therefore, we conclude that system safety concepts can be useful aids for policymakers who aim to improve ML system safety.

Design of a tea strip-lining degree discrimination system based on appearance characteristics and near-infrared spectroscopy

Design of a tea strip-lining degree discrimination system based on appearance characteristics and near-infrared spectroscopy

Design of ECG Circuit and Patient Remote Monitoring System Using MQTT Protocol for ECG Signals

In the healthcare sector, leveraging the Internet of Things (IoT) brings significant advantages, especially in remote monitoring of patients' health using electrocardiogram (ECG) signals. This study employs IoT technology to enable remote ECG monitoring, utilizing the MQTT (Message Queuing Telemetry Transport) protocol to establish a web server. The dedicated measuring device integrates the ADAS1000-3 IC with ESP32 for real-time ECG signal acquisition. In this study, we utilize the Pan-Tompkins algorithm to extract QRS complexes from electrocardiographic signals, count heartbeats, and early detect cardiovascular abnormalities. This solution was tested on a sample dataset from the MIT-BIH (Massachusetts Institute of Technology - Beth Israel Deaconess Medical Center) database. Finally, the article proposes to design and develop a device using advanced IC technologies such as IC ADAS1000-3 combined with ESP32. This device will be designed to meet the requirements of real-time processing and data transmission via webserver.

Design of wireless charging system for E-Vehicle

To address the dual problems of fuel reliance and air pollution, this study describes the design of a wireless ground to vehicle charging system powered by solar energy and specifically designed for electric vehicle (EV) charging stations. As the number of electric vehicles on the road steadily rises, they present a viable way to cut travel expenses by switching from conventional fuel to electricity, which is a more economical and sustainable option. With the introduction of a wireless EV charging system, this creative solution does away with the need for external power sources and permits continuous charging without interfering with driving. Utilizing solar power, the charging system incorporates solar panels, batteries, circuit regulators, boost converters, receiver and transmitter copper coils, AC/DC converters, microcontrollers (such as ATmega), LCD screens, and circuit regulators. This study describes a technique that shows that charging electric cars while driving is feasible and eliminates the need to stop. This technology for wireless solar electric vehicle charging presents a forward-thinking approach to sustainable mobility by providing a workable solution that can be easily included in the road infrastructure. For the wireless charging, in addition, various coil designs are suggested.

Design and Implementation of an Interactive Embedded System as a Low-Cost Remotely Operated Vehicle for Underwater Applications

The underwater environment is harsh and challenging for human life, prompting companies and researchers to develop advanced technologies for exploration. Building on previous work that applied a CNN-based method for underwater object classification, this paper focuses on the design and implementation of an interactive embedded system for a compact Remotely Operated Vehicle (ROV) with specific dimensions and weight. The primary goal is to capture real-time underwater video using remote control communication via Ethernet. The ROV is powered by five brushless motors controlled by a smart PID controller. Precise pulse-width modulation signals enhance stability during movements along three axes, enabling high-resolution video capture. The system utilizes the Raspberry Pi 3's computing power for motion control, positioning, temperature monitoring, and video acquisition. Experimental results demonstrate the system's capability to process 42 frames per second. A user-friendly graphical interface allows for remote ROV control across various operating systems. With a depth rating of 100 meters and speed of 0.148 m/s. This ROV surpasses human divers' limitations and holds significant potential for applications in surveillance, operations, maintenance, and measurement tasks underwater. The proposed ROV contributes to the advancement of underwater exploration technology by combining high performance with cost-effectiveness.

Survivorship, complications, and functional outcomes of uncemented distal femoral endoprosthesis with short, curved stem for patients with bone tumours.

Endoprosthetic reconstruction following distal femur tumour resection has been widely advocated. In this paper, we present the design of an uncemented endoprosthesis system featuring a short, curved stem, with the goal of enhancing long-term survivorship and functional outcomes. This study involved patients who underwent implantation of an uncemented distal femoral endoprosthesis with a short and curved stem between 2014 and 2019. Functional outcomes were assessed using the 1993 version of the Musculoskeletal Tumour Society (MSTS-93) score. Additionally, we quantified five types of complications and assessed osseointegration radiologically. The survivorship of the endoprosthesis was evaluated according to two endpoints. A total of 134 patients with a median age of 26 years (IQR 16 to 41) were included in our study. The median follow-up time was 61 months (IQR 56 to 76), and the median functional MSTS-93 was 83% (IQR 73 to 91) postoperatively. Overall, 21 patients (16%) encountered complications, and the rate of aseptic loosening was 7% (9/134). The survival rate up to 8.5 years was 93% for aseptic loosening as the endpoint, and 88% for any reason as the endpoint, retrospectively. The use of an uncemented distal femoral endoprosthesis with a short, curved stem demonstrated a low incidence of aseptic loosening and achieved long-term survivorship of up to nine years. Meanwhile, aseptic loosening typically occurs in the early stage postoperatively.

Storage stability and antioxidant activity of astaxanthin and beta-carotene as affected by the architecture of O/W emulsions of milk proteins

Astaxanthin and β-carotene are among the potential anti-oxidant carotenoids with strong health promoting features. However, their sensitivity to light, heat and mechanical forces limit their application in food processing and storage conditions. The design of food matrix system, such as emulsions, that could prevent them from exposure to environmental factors will be utmost importance to slow down their degradation. In this study, O/W emulsion systems containing sunflower oil were prepared by applying ultrasound treatment and using different stabilizers (i.e. whey protein isolate (WPI) and sodium caseinate; NaCAS), co-surfactants (pectin and lecithin) while their performance against physical and chemical stability were investigated. The WPI– lecithin system produced emulsion droplets with Average diameter in the range of 560 – 2900 nm with a zeta-potential in the range of (-31) – (-34) mV, whereas the NaCAS – lecithin systems produced emulsion droplets with average diameter in the range of 230 – 240 nm and zeta potential of (–44) – (-48) mV. These values have a direct effect on the physical stability and chemical stability of the carotenoids in the emulsions. Astaxanthin was found to be more sensitive than β-carotene to ultrasound processing conditions. The designed emulsion systems slowed down the degradation of the carotenoids and maintained their DPPH scavenging properties during storage with astaxanthin loaded emulsions exhibiting better DPPH activity than β-carotene loaded emulsions.

Design and optimal scheduling of forecasting-based campus multi-energy complementary energy system

This study presents a complete campus multi-energy complementary energy system (MCES), including an accurate forecasting model, efficient MCES model, and effective multi-objective optimal scheduling strategy to better utilize renewable energy. A hybrid forecasting model, including multi-scale mathematical morphological decomposition, a bidirectional long short-term memory network, and subsequences to the original sequence (S2O) manner based on the rolling approach (RA), is utilized to forecast renewable energy variations. RA continuously updates input datasets to improve forecasting accuracy. Decomposition and forecasting modules are employed in an S2O manner to reduce the number of required modules and forecasting cost. The volatility of renewable energy is mitigated by supplementing energy sources with storage. During operation, the conversion times of different energies are reduced by reasonably planning the energy supply sequence based on different loads on the demand side, increasing the energy utilization rate. The proposed multi-objective optimal scheduling strategy includes a stacked multilevel-denoising autoencoder, non-dominated sorting genetic algorithm-II, and deep reinforcement learning (DRL) for surrogate-model building, Pareto frontier establishment, and optimal solution selection. This is the first study to use DRL to select the final optimal solution. A performance comparison confirms the proposed model effectively decreases costs and pollution while increasing thermal comfort.

Typical daily scenario extraction method based on key features to promote building renewable energy system optimization efficiency

Given its intrinsic volatility and randomness, the effective design of a building renewable energy system (BRES) necessitates long-period, short-step boundaries. Nevertheless, this poses computational burdens. In this context, time-series aggregation (TSA) emerges as a valuable approach, which involves obtaining a set of typical scenarios to substitute original time series. Numerous studies have focused on mathematical algorithms to minimize the deviation between typical scenarios and original time series. However, the premise of enhancing model-solving efficiency by extracting typical scenarios is to ensure the optimality of planning. Henceforth, identifying and quantifying key features in the original time series that significantly influence the planning, and further extracting need to be addressed. This paper proposes a typical day extraction method based on key features, named the feature-based method. Key features are summarized through an extensive literature review and then quantitatively expressed through different indicator models. Finally, a feature-based mixed integer linear programming (MILP) model is established for extraction. To evaluate the effectiveness of the feature-based method, it is applied to design a BRES and compared with traditional TSA methods such as Averaging Method (AM) and k-means. Results indicate that the feature-based method can effectively retain key features, thereby achieving more efficient and accurate designs.

Design and Development of Attendance Management and Analysis System using LLM

Design and Development of Attendance Management and Analysis System using LLM

Climate system design methodology for a new family of unified combine harvesters

BACKGROUND: Designing a combine harvester cabin climate system is a complex and multi-stage process that includes solving a set of tasks. To solve the tasks set, a design engineer must have knowledge and skills in various fields, starting with thermal engineering calculations and ending with experimental research methods and computer modeling, which requires a large amount of intellectual and time resources. Therefore, the task of creating a unified combine harvester cabin climate system design methodology is very relevant. AIMS: The purpose of this work is the calculation and design of a climate system for a unified cabin of a grain harvester and a forage harvester. The designed climate system is designed to create a comfortable microclimate in the cabin of the combine for 2 people in summer and winter operating conditions. METHODS: The methodology of development of combine harvester cabin climate system is considered, which includes the development of engineering calculation methods and mathematical modeling of thermodynamic and ventilation processes in the cabin. RESULTS: The main parameters were determined: heat intakes and heat losses for the grain harvester and forage harvester cabins, which amounted to 2.8 and 2.2 kW for the grain harvester; 2.9 and 2.35 kW for the forage harvester; the required air flow rate supplied to the cabin to ensure a comfortable temperature – 740 m3/h; the percentage of air recirculation from the conditions of absence of fogging and creation of overpressure in the cabin – 75 %; the cooling and heating capacity of the climate system, taking into account the operating conditions of the combine, is 7.8 and 6.3 kW, respectively. The selection of the main equipment of the climate system for a unified cabin for a new family of unified cabins of combines – evaporator-heater BUHLER 1000 MFWD; compressor Valeo TM16. CONCLUSIONS: Designed in accordance with the presented climate system methodology, it will ensure a comfortable cabin air temperature in the range of 22-24 °C under various operating modes of the combine in different regions. In addition, the climate system parameters will eliminate fogging of the cabin windows and, due to the created overpressure, the penetration of dusty air inside.

Design and Development of an IoT-Based Leakage Detection System for Kitchens

In Nigeria, the frequent loss of lives and properties due to LPG usage for cooking necessitates urgent intervention. This paper presents an Internet of Things (IoT) based detection system to monitor LPG leakage in kitchens. Key components include a solenoid valve for gas flow control, an MQ6 sensor for LPG monitoring, and an STM32F411CEU6 microcontroller, programmed in embedded C, as the central processing unit of the system. The system introduces an emergency shutdown mechanism using an MMBT3904 NPN transistor. Upon detecting a leak, the MQ6 sensor informs the microcontroller, which prompts the user with a Short Message Service (SMS) and calls, and then triggers the transistor to close the solenoid valve if the leakage poses an explosion risk, thus preventing potential disasters. Additionally, a custom mobile app allows for remote monitoring and control of LPG leakage. To address Nigeria's unreliable electricity supply, the system includes a 5V power pack, a solar panel, and a battery for continuous power supply to the system. Deploying this system will significantly reduce the risks associated with LPG leaks in kitchens across Nigeria.

Personalized low-cost thermal comfort monitoring using IoT technologies

Thermal comfort plays an essential role in the well-being and productivity of occupants. Typically, thermal comfort is assessed either through surveys completed by building occupants or through sensor data that is analyzed using thermal comfort models. Automating comfort surveys and data collection processes reduce the risk of information loss, providing more accurate and personalized thermal comfort assessments over longer periods of time. To this end, this paper presents the design and implementation of a thermal comfort monitoring system consisting of low-cost hardware components and using IoT technologies. The system consists of intelligent wireless sensor nodes that collect and process environmental data, a portable main station that integrates and stores data, and a digital survey that provides feedback from building occupants. To ensure accuracy, the low-cost hardware components of the intelligent sensor nodes are calibrated in a climate chamber, using high-precision sensors for reference. After calibration, the system is deployed in a field test where several intelligent sensor nodes collect environmental data in an office, while occupants complete the digital thermal comfort survey. In addition, thermal comfort indexes are computed by the intelligent sensor nodes and compared with the feedback of each building occupant. The results indicate that the low-cost thermal comfort monitoring system successfully collects and integrates thermal comfort data from the intelligent sensor nodes and the digital survey, being able to create personalized thermal comfort profiles. In future work, the system can be used in large-scale thermal comfort surveys, to develop personalized thermal comfort models and to control personalized comfort systems.

SCADA System for Process Data Exchange in Master Slave RF and Iot Network

In every aspect of our lives, we can observe how automation plays a facilitating role in the performance of responsibilities of a comprehensive rank. Thanks to the development of electronics, today's process measurement systems enable the measurement values of process quantities to be visualized on display, sent remotely, and stored in a file, compatible with future user processing. In this paper are represented results of the development, design and practical implementation of the SCADA system, which enables data exchange between SCADA screen, MASTER and SLAVE stations. The MASTER station is installed in one industrial plant and the SLAVE is installed in another plant. The SCADA monitoring system is built into the MASTER station. The SLAVE station is connected by RF (radio frequency connection) to the MASTER station. In the MASTER and SLAVE station two microcomputers units, as well as appropriate RF modules, are installed. The microcomputer in the MASTER station is serially connected to the IoT microcomputer, for the transfer of measurement data in the Internet network. The solution provides visualization, data log file, and transfer to the IoT network of process data from the two industrial plants.

Design of intelligent manufacturing monitoring system for internet of things based on encryption technology and intrusion detection technology

With the rapid development of intelligent manufacturing, energy consumption and safety issues have gradually attracted widespread attention. Heat loss not only leads to energy waste, but also may affect the efficiency of equipment operation and production safety. Therefore, this study aims to design an efficient iot intelligent manufacturing monitoring system, focusing on the synergistic effect of heat loss energy management and intrusion detection to improve energy utilization efficiency and optimize security monitoring. The system uses a sensor network to monitor the heat loss in the manufacturing environment in real time, and evaluates the energy efficiency performance combined with data analysis algorithms. Through intrusion detection technology, real-time monitoring of system security, using machine learning models to identify abnormal behavior. Integrate heat loss management and security monitoring to build an intelligent manufacturing platform based on the Internet of Things. The experimental results show that the monitoring system can effectively reduce the energy heat loss and improve the accuracy of intrusion detection. The implementation of the system significantly improves the energy efficiency and safety of the production line, reducing potential safety hazards. Therefore, the iot intelligent manufacturing monitoring system designed in this paper effectively integrates heat loss energy management and intrusion detection technology, and provides an efficient solution for intelligent manufacturing.

Design and Operational Features of an Active Condensation System for Enhanced Energy Efficiency in a Biomass-Fired District Heating Plant

Design and Operational Features of an Active Condensation System for Enhanced Energy Efficiency in a Biomass-Fired District Heating Plant

Data-driven farming: implementing internet of things for agricultural efficiency

<p>Integrating internet of things (IoT) technology into agriculture has become essential to address challenges such as low crop productivity, which is often due to insufficient soil nutrients and suboptimal environmental conditions. This paper discusses the design and implementation of an IoT-based system for agriculture that aims to automate key parameters, facilitate real-time monitoring, and promote sustainable practices. Equipped with a graphical user interface (GUI), the system focuses on improving irrigation, regulating temperatures, and correcting soil nutrient deficiencies to improve crop productivity. Our research includes the use of humidity sensors to monitor soil moisture and temperature sensors. Soil nutrient levels, especially nitrogen, phosphorus, and potassium (NPK), were also assessed. We conducted experiments on three radish varieties using this IoT system and compared the results with traditional farming methods. The germination rate was impressive, reaching 98% within the first four days, while in a traditional greenhouse, it did not exceed 50%. As for plant height and leaf area, the smart greenhouse was better. These results were promising and demonstrated the potential of IoT in enhancing agricultural productivity. These results highlight the significant impact of IoT technology in enhancing agricultural productivity and its potential for broader application in this sector.</p>

Optimum error design and 5-axis CNC machining of preloaded roller-gear-cam in roller-drive system

Roller-gear-cam (RG-cam) drive systems in machine tool rotary tables provide enhanced rotation accuracy and superior transmission efficiency due to their preloaded design. However, achieving a controllable preload engagement between the roller and RG-cam requires the use of 5-axis CNC machining using a non-formed tool, which inevitably introduces machining errors. Accordingly, this study proposes a comprehensive method for the design, analysis, and manufacture of a precision RG-cam system on a conventional 5-axis CNC machine tool. Notably, the proposed method enables the machining errors to be controlled and minimized. The feasibility of the proposed approach is demonstrated by machining a prototype roller-drive system on a 5-axis CNC machine. The accuracy of the machined RG-cam is evaluated in accordance with the ISO 230-2 standard. It is shown that the forward and reverse rotational errors of the RG-cam over a single revolution are less than 10 s.

Bi-Level Optimal Design for DC Traction Power Supply System With Reversible Substations

Bi-Level Optimal Design for DC Traction Power Supply System With Reversible Substations

Design and Experimental Evaluation of a Leader-follower Robot-assisted System for Femur Fracture Surgery

Design and Experimental Evaluation of a Leader-follower Robot-assisted System for Femur Fracture Surgery