Modern Systems Research Articles

Finding a closest saddle–node bifurcation in power systems: An approach by unsupervised deep learning

We propose a neural network using an unsupervised learning strategy for direct computation of closest saddle–node bifurcations, eliminating the need for labeled training data. Our method not only estimates the worst-case load increase scenarios but also significantly reduces the computational complexity traditionally associated with this task during inference time. Simulation results validate the effectiveness and real-time applicability of our approach, demonstrating its potential as a robust tool for modern power system analysis.

Unveiling the Potential of Spirulina Biomass—A Glimpse into the Future Circular Economy Using Green and Blue Ingredients

The present work aims to explore Spirulina biomass’ functional and technological marvels and its components, such as C-phycocyanin (C-PC), in modern food systems from a circular economy perspective, evaluating a decade of insights and innovations. This comprehensive review delves into the pivotal studies of the past decade, spotlighting the vital importance of maintaining stability in various food matrices to unleash the full biological impacts. Through the lens of food science intertwined with circular economy principles, this analysis meets health and environmental requisites and explores the harmonious synergy between food systems, economy, and industry. While Spirulina has typically served as a supplement, its untapped potential as a fundamental food ingredient has been unveiled, showcasing its abundant nutritional and functional attributes. Technological hurdles in preserving the vibrant color of C-PC have been triumphantly surmounted through simple temperature control methods or cutting-edge nanotechnology applications. Despite the gap in sensory acceptance studies, the emergence of blue foods introduces groundbreaking functional and innovative avenues for the food industry.

Unveiling the Potential of Spirulina Biomass—A Glimpse into the Future Circular Economy Using Green and Blue Ingredients

The present work aims to explore Spirulina biomass’ functional and technological marvels and its components, such as C-phycocyanin (C-PC), in modern food systems from a circular economy perspective, evaluating a decade of insights and innovations. This comprehensive review delves into the pivotal studies of the past decade, spotlighting the vital importance of maintaining stability in various food matrices to unleash the full biological impacts. Through the lens of food science intertwined with circular economy principles, this analysis meets health and environmental requisites and explores the harmonious synergy between food systems, economy, and industry. While Spirulina has typically served as a supplement, its untapped potential as a fundamental food ingredient has been unveiled, showcasing its abundant nutritional and functional attributes. Technological hurdles in preserving the vibrant color of C-PC have been triumphantly surmounted through simple temperature control methods or cutting-edge nanotechnology applications. Despite the gap in sensory acceptance studies, the emergence of blue foods introduces groundbreaking functional and innovative avenues for the food industry.

Construction of the structure of rural electric networks taking into account the availability of renewable sources of electricity

It is noted that although the share of renewable electricity sources continues to grow and develop rapidly, the industry still faces many challenges, including how to continue to reduce electricity losses and annualized costs, improve operation and maintenance efficiency, maintain grid stability, ensure safety and reliability of the power supply system containing renewable sources of electrical energy. It is shown that an effective solution to these problems is possible only on the basis of the analysis of the prospects for the development of local electric power systems that contain renewable sources of electric energy, the development of technical and organizational support mechanisms that will contribute to the construction of modern system (schematic) solutions. It is proposed to apply the potential surface method for building the optimal structure of the rural electrical network during its design and modernization in the presence of renewable sources of electrical energy, which allows optimizing the structure of the network from the point of view of electrical energy losses and reducing annualized costs. The algorithm for forming the structure of power supply systems is described, which implements the simultaneous solution of the tasks of determining the number of load nodes, distributing electrical receivers between them, determining the design of power sources, taking into account the discreteness of the design of system elements. The results of numerical modeling based on the proposed algorithm are presented on the example of solving the problem of reconstruction of a section of the rural electricity network.

Construction of the structure of rural electric networks taking into account the availability of renewable sources of electricity

It is noted that although the share of renewable electricity sources continues to grow and develop rapidly, the industry still faces many challenges, including how to continue to reduce electricity losses and annualized costs, improve operation and maintenance efficiency, maintain grid stability, ensure safety and reliability of the power supply system containing renewable sources of electrical energy. It is shown that an effective solution to these problems is possible only on the basis of the analysis of the prospects for the development of local electric power systems that contain renewable sources of electric energy, the development of technical and organizational support mechanisms that will contribute to the construction of modern system (schematic) solutions. It is proposed to apply the potential surface method for building the optimal structure of the rural electrical network during its design and modernization in the presence of renewable sources of electrical energy, which allows optimizing the structure of the network from the point of view of electrical energy losses and reducing annualized costs. The algorithm for forming the structure of power supply systems is described, which implements the simultaneous solution of the tasks of determining the number of load nodes, distributing electrical receivers between them, determining the design of power sources, taking into account the discreteness of the design of system elements. The results of numerical modeling based on the proposed algorithm are presented on the example of solving the problem of reconstruction of a section of the rural electricity network.

Application of root cause analysis and TEAMSTEPPS post intravesical gas explosion during transurethral resection of the prostate: a rare case report

BackgroundAn intravesical gas explosion is a rare complication of transurethral resection of the prostate (TURP). It was first reported in English literature in 1926, and up to 2022 were only forty-one cases. Injury from an intravesical gas explosion, in the most severe cases appearing as extraperitoneal or intraperitoneal bladder rupture needed emergent repair surgery.Case presentationWe present a case of a 75-year-old man who suffered an intravesical gas explosion during TURP. The patient underwent an emergent exploratory laparotomy for bladder repair and was transferred to the intensive care unit for further observation and treatment. Under the medical team’s care for up to sixty days, the patient recovered smoothly without clinical sequelae.ConclusionsThis case report presents an example of a rare complication of intravesical gas explosion during TURP, utilizing root cause analysis (RCA) to comprehend causal relationships and team strategies and tools to improve performance and patient safety (TeamSTEPPS) method delivers four teamwork skills that can be utilized during surgery and five recommendations to avoid gas explosions during TURP to prevent the recurrence of medical errors. In modern healthcare systems, promoting patient safety is crucial. Once complications appear, RCA and TeamSTEPPS are helpful means to support the healthcare team reflect and improve as a team.

A Lightweight Sequential Convolutional Neural Network for Smart Grid Stability Analysis

A Smart grid stability analysis is essential for ensuring modern power systems' reliable and secure operation. This approach helps identify potential instabilities and disturbances that can lead to blackouts or equipment failures. By analyzing the stability of the grid, operators can take proactive measures to maintain a stable and resilient power infrastructure. Monitoring smart grid data from various sources and analyzing how to control the stability of the grid are challenging tasks. A convolutional neural network (CNN) can effectively capture spatial dependencies and patterns from grid data and can help in enabling accurate prediction and classification of stability-related events in a power system. However, developing a CNN that has fewer learnable parameters and provides high accuracy is challenging. This paper presents a sequential CNN architecture to detect the stability of the Decentral Smart Grid Control (DSGC) system. A mathematical model of the 4-node start architecture of a smart grid was presented. Later, 12 parameter-based grid datasets from the UCI repository were used to validate the proposed network. The proposed CNN accepts sequential data to capture temporal dependencies in the data. The sequential process in a single dimension offers fewer learnable parameters, making the network more compact and computationally efficient. The proposed 11-layered CNN has a total of 12.7K learnable parameters. The detailed analysis of the proposed CNN using ambiguity and the t-SNE score suggested that the model can identify discriminative features for classifying data into stable and unstable classes. A comparison analysis of the quantitative parameters revealed that the model performed well, with 98.82%, 98.55%, 98.88%, and 98.77% accuracy, precision, recall, and F1 score, respectively.

COMPARISON OF MESSAGE PASSING SYSTEMS IN CONTEXT OF ADAPTIVE LOGGING METHOD

Computer software is an important part of technological progress. As it becomes more and more complex and sophisticated, so does the need to protect it. Apart from typical information security aspects of integrity, availability and confidentiality, the scale and complexity of modern computer systems require a high level of control and observability. The main goal of this research is to build upon the foundations laid by the general idea of an adaptive logging method and introduce the next iteration of its design in the form of an appropriate message passing system to be used to propagate required changes to corresponding implementation in an effective and performant manner. Four different message passing system models are introduced, based on different technologies such as RabbitMQ message broker, communication channels in PostgreSQL database management system, general web server architecture and Linux-based process signaling interface. For each of those an overview description and graphical model is presented. Finally, the resulting comparison is conducted, comparing aspects such as reliance on third-party software, communication medium, error surface increase and authentication related considerations. As a result, the design based on process signaling approach is determined to be the most suitable for adaptive logging method, as it does not introduce any third-party software (and as such affects error surface in a somewhat negligible manner), binds directly to an observed application, is built using low level concepts that should be present in multiple different platforms and programming languages and should be able to reuse authentication logic that is already used when accessing computational machine where observed program is executed.

State of the Art and Future Trends in Monitoring for Industrial Induction Heating Applications

Induction surface hardening (ISH) processes are widely used in the heat treatment of numerous industrial components, especially in the automotive industry. Since this industry operates under very demanding quality standards, it is crucial for these heat treatment processes to meet rigorous specifications to ensure the safety and reliability of the produced components. This implies the precise and repeatable control of certain parameters throughout the manufacturing process of each of the parts treated, through precise and reliable instrumentation in electromagnetically harsh environments. The main objective of this work is to define the monitoring process for an industrial IHS application, determining the control needs and the methods of measurement, recording, and verification of the parameters that ensure the quality of the process. This paper describes the monitoring process of induction surface hardening, emphasizing the use of sensors and modern measurement and control systems. A comprehensive monitoring system supported by a programmable logic controller (PLC) and mixed acquisition and instrumentation systems (analog and digital) implemented with a high-performance Field Programmable Gate Array (FPGA) will be presented.

Surrogate-model-based dwell time optimization for atmospheric pressure plasma jet finishing

With the rapidly increasing requirements for high-precision optical elements in modern optical systems, atmospheric pressure plasma jet (APPJ) finishing has attracted wide attention due to its high material removal efficiency and superior surface quality. However, the temperature fluctuation in the localized processing footprint, induced by the heated plasma and varying dwell time, emerges as a critical factor that oscillates the etching rate and thus degrades the deterministic removal control. Existing dwell time solution algorithms only apply under the assumption that the material removal rate exhibits temporal and spatial constancy, which is not the case in APPJ finishing. To address this challenge, a surrogate-model-based dwell time optimization approach for the temperature-dependent tool influence function (TIF) is proposed in this study. Firstly, a temperature-dependent TIF is proposed to quantitatively describe the thermal effect, and the corresponding calibration method is established. Subsequently, a thermodynamic simulation model is established to simulate the temperature of the localized processing footprint. By integrating the temperature-dependent TIF with the temperature prediction model, a novel dwell time optimization algorithm is proposed to alleviate the error caused by the temperature fluctuation. Given the prohibitive long computation time of the thermodynamic simulation model, a data-driven surrogate model based on Long Short Term Memory (LSTM), capable of rapid temperature prediction, is trained through the customized simulation dataset. The effectiveness of the proposed surrogate-model-based dwell time optimization algorithm is verified through simulations and experiments. Compared to traditional dwell time solution algorithms, the proposed approach yields a 47.12% improvement in machining precision.

Fusing linguistic and acoustic information for automated forensic speaker comparison

Verifying the speaker of a speech fragment can be crucial in attributing a crime to a suspect. The question can be addressed given disputed and reference speech material, adopting the recommended and scientifically accepted likelihood ratio framework for reporting evidential strength in court. In forensic practice, usually, auditory and acoustic analyses are performed to carry out such a verification task considering a diversity of features, such as language competence, pronunciation, or other linguistic features. Automated speaker comparison systems can also be used alongside those manual analyses. State-of-the-art automatic speaker comparison systems are based on deep neural networks that take acoustic features as input. Additional information, though, may be obtained from linguistic analysis. In this paper, we aim to answer if, when and how modern acoustic-based systems can be complemented by an authorship technique based on frequent words, within the likelihood ratio framework. We consider three different approaches to derive a combined likelihood ratio: using a support vector machine algorithm, fitting bivariate normal distributions, and passing the score of the acoustic system as additional input to the frequent-word analysis. We apply our method to the forensically relevant dataset FRIDA and the FISHER corpus, and we explore under which conditions fusion is valuable. We evaluate our results in terms of log likelihood ratio cost (Cllr) and equal error rate (EER). We show that fusion can be beneficial, especially in the case of intercepted phone calls with noise in the background.

Intelligent Diagnosis Method of Transformer Based on Oil Chromatographic Data

Abstract As one of the most important equipment in the operation of power system, ensuring the safe and stable operation of power transformer is a prerequisite for ensuring the normal supply of power grid. The failure of the power transformer will lead to the interruption of the power supply of the power grid and cause huge losses to the national economy. With the rapid development of China’s power grid scale and the continuous improvement of the intelligent construction of modern power system, the number of power equipment such as transformers is increasing. Therefore, it is necessary to make timely and accurate judgment on the status of key power transformation and distribution equipment in the power system, and grasp the operation of electrical equipment in real time, so as to ensure the reliability of power supply in the power system. This paper takes 220kV oil-immersed transformer as the research object. This paper considers the problems and characteristics of the current transformer fault diagnosis methods. By making full use of dissolved gas analysis (DGA) information in oil, a method for diagnosing faults in electrical transformers based on particle swarm optimization and generalized regression neural networks has been proposed. The simulation comparison experiment is carried out to select the most suitable transformer intelligent diagnosis method based on oil chromatographic data.

Mission-oriented capability evaluation for combat network based on operation loops

With continuous growth in scale, topology complexity, mission phases, and mission diversity, challenges have been placed for efficient capability evaluation of modern combat systems. Aiming at the problems of insufficient mission consideration and single evaluation dimension in the existing evaluation approaches, this study proposes a mission-oriented capability evaluation method for combat systems based on operation loop. Firstly, a combat network model is given that takes into account the capability properties of combat nodes. Then, based on the transition matrix between combat nodes, an efficient algorithm for operation loop identification is proposed based on the Breadth-First Search. Given the mission-capability satisfaction of nodes, the effectiveness evaluation indexes for operation loops and combat network are proposed, followed by node importance measure. Through a case study of the combat scenario involving space-based support against surface ships under different strategies, the effectiveness of the proposed method is verified. The results indicated that the ROI-priority attack method has a notable impact on reducing the overall efficiency of the network, whereas the O-L betweenness-priority attack is more effective in obstructing the successful execution of enemy attack missions.

Optimized integration of renewable energy sources using seven-level converter controlled by ANFIS-CS-GWO

The global energy landscape is undergoing a significant transformation, driven by the need to reduce greenhouse gas emissions, enhance energy security, and provide sustainable and clean power solutions. Renewable energy sources (RES) such as Solar Photovoltaic (SPV), wind, fuel cells, and tidal power play a crucial role in this transition due to their abundant availability and low environmental impact. However, integrating these diverse energy sources into power systems requires advanced control strategies to ensure stability, efficiency, and reliability. Existing systems, however, face significant challenges such as unstable voltage profiles, prolonged fault settling time (FST), and high total harmonic distortion (THD), which compromise their efficiency and reliability. To overcome these limitations, a novel methodology is proposed, integrating Solar PV (SPV), wind, fuel cell, and tidal energy sources into a seven-level converter (SLC) system, which is named as Four Level RES based SLC (FLRES-SLC) system. This system is managed by an Adaptive Neuro-Fuzzy Inference System (ANFIS), fine-tuned by Cuckoo Search adopted Grey Wolf Optimizer (CS-GWO). This hybrid control strategy ensures a robust voltage profile, significantly reduces FST, and lowers THD, thereby markedly improving the overall performance of the converter system. The innovative approach offers a reliable and efficient solution for modern power systems, enhancing their integration with diverse RES and ensuring high-quality power delivery.

Does digitalization improve supply chain efficiency?

Enhancing supply chain efficiency (SCE) is crucial for establishing a modern supply chain system. This study determines that (I) supply chain digitalization (SCD) has a significant positive impact on enhancing SCE; (II) SCD reduces information asymmetry between enterprises, driving all entities within the chain toward digital transformation, which improves overall SCE; and (III) state-owned enterprises, those with strong internal controls, and companies with relatively dispersed supply chain concentration, SCD significantly improves SCE.

Novel Nanocomposite based Microstrip Patch Antenna for C and X band Applications

Microstrip patch antennas (MPAs) are essential components in modern wireless communication systems, and their design is significantly influenced by the properties of the materials used. In this study, we investigate the impact of incorporating composites of fumed silica within RT-Duroid dielectric material on the performance of microstrip patch antennas. The effective permittivity of the composite is calculated using the Maxwell-Garnett model. Through systematic theoretical studies, we analyze the performance of composite antennas with varying composite volumes and dielectric substrate thicknesses. Utilizing HFSS 13.0 software, we optimize the substrate thickness and antenna dimensions to design MPAs resonating at 10 GHz. The proposed antenna, with dimensions of 11.85 mm by 9.044 mm for the patch and 50 mm by 50 mm for the substrate, exhibits superior performance compared to conventional antennas, achieving a higher gain of 10 dB, good efficiency of 96% along with favorable return loss characteristics. The frequency ranges of the C and X bands are better suited for military applications, weather radar, satellite communication, air traffic control, maritime radar, and remote sensing. This research underscores the significance of incorporating composite materials into MPA design to enhance performance and broaden application potential in modern communication technologies.

Deep deterministic policy gradient for adaptive power system stabilization and voltage regulation

This paper focuses on the challenges posed by the time-variable and nonlinear nature of energy systems. In this systems set points fluctuate due to load variations, generator losses, and disturbances such as short circuit faults. Maintaining power system stability and effectively damping low-frequency oscillations are crucial objectives. To enhance damping and dynamic stability in modern systems power system stabilizers (PSSs) are typically employed. These facilities generate auxiliary control signals for the generator excitation system, diminishing oscillations. In this study, by involving a deep deterministic policy gradient (DDPG) based on a synergetic control approach to the excitation system of synchronous generators, a novel approach to achieving fast and adaptive stability improvement and voltage regulation is suggested. The presented technique incorporates an intelligent synergetic PSS that effectively stabilizes the voltage response of a single machine connected to an infinite bus power system. To leverage its adaptive capability in controller parameter adjustments, an auxiliary deep DDPG algorithm with an Actor-Critic architecture is designed. The suggested procedure offers a promising avenue for effectively handling electromechanical oscillations and achieving improved voltage regulation and transient stability in power systems.

RESEARCH TREND ON IMPROVEMENT OF DIELECTRIC RESONATOR ANTENNA: A BIBLIOMETRIC ANALYSIS

Dielectric Resonator Antennas (DRAs) have components in modern wireless communication systems due to their compact size, high radiation efficiency, and frequency versatility. With escalating demands for higher data rates and spectrum efficiency, the enhancement of DRAs has garnered significant attention in antenna research. Despite the promising characteristics of DRAs, there exists a gap in providing a consolidated overview of the state-of-the-art advancements and emerging themes in DRA research. This study aims to bridge this gap by conducting a meticulous bibliometric analysis to delineate the research trends, highlight gaps, and guide future investigations. Our methodology involves a systematic search and analysis of scholarly publications related to the improvement of DRAs. We use bibliometric tools, citation analysis, and co-authorship networks to explore publication trends, influential authors, and collaborative networks within the DRA research community. By identifying emerging themes, key contributors, and collaborative networks, this study is poised to inform researchers, practitioners, and policymakers about the prevailing trends in the field. The insights gained are expected to guide future research directions, foster interdisciplinary collaboration, and contribute to the continued evolution of DRAs. In conclusion, this bibliometric analysis aims to provide a roadmap for navigating the intricate research landscape on the improvement of DRAs. The anticipated outcomes hold the potential to drive innovation, inspire collaborative initiatives, and contribute to the ongoing advancement of DRAs in the rapidly evolving realm of wireless communication systems.

『기생 및 창기에 관한 서류철』의 원(原)자료 재구성에 관한 연구: 대한제국 말기 ‘전문 예술인 기생’에 대한 일제 통감부의 정책과 그 변화과정

This study targeted a total of 58 pages of Gisaeng-related documents, including all Gisaeng -related documents for [Records of Gisaeng and Prostitute] and 3 pages of [Agenda on Gisaeng and Prostitute Regulation Enforcement Instructions] that did not exist in the original copies and were newly discovered during the preliminary research. Then, this study examined each document and reorganized them based on the time sequence of documentation for a precise and detailed clarification on the details and process of changes in the policies that the Resident-General enforced on the Gisaeng, who was a professional performer during the late Korean Empire. First, [Gisaeng Regulation Law] began the modern Gisaeng system from September 25, 1908. The Resident-General first set to investigate and identify the personal information of the official Gisaengand transfer them to the Metropolitan Police Department. Second, the Resident-General enacted and confirmed the [Gisaeng Regulation Law] on September 15, 1908. As the internal official, Byungjun Son, approved the law on September 24, 1908, the [Gisaeng Regulation Law] was enforced on September 25, 1908, as the beginning of the modern Gisaeng system. Third, the Resident-General established a legal and political system for modernized control and management of Gisaengs (including official Gisaeng) who were working under the existing traditional Gisaeng system by enacting the series of Gisaeng-related laws and regulations, including [Gisaeng Regulation Law], [Standard Regulation of Gisaeng Association], [Proclamation on Gisaeng] in September 1908. Fourth, the Resident-General enacted the [Notice on Gisaeng and Prostitute Regulation Law Enforcement] and its detailed guidelines, [Detailed Notice on Gisaeng and Prostitute Regulation Law Enforcement] in October 1908, as the guidelines to be aware and conducted by the Japanese police officers of each local police station and each police station branch. The Resident-General also arranged and confirmed the formats for reporting operation, closure, address change, etc., of Gisaeng and prostitutes. Lastly, Gisaengs who were travelling for local tour, overseas tour, or other performances were required by the Resident-general to submit detailed contracts, contract details, and petition with names and addresses of Gisaeng and all performers. These documents were used to establish a modern management and control system for determining the authorization. This Gisaeng management and control system organized by the Resident-General during the late Korean Empire developed more systematically by the beginning of the Japanese colonial rule.

Impact of ABT based control strategies on ALFC and AVR in DFIG-integrated interconnected power systems

To maintain a stable electricity supply in interconnected power grids, effective frequency and voltage regulation are required. Interconnected power systems use Automatic Load Frequency Control (ALFC) and Automatic Voltage Regulator (AVR) for this purpose. The integration of renewable sources, such as Doubly Fed Induction Generators (DFIGs), creates challenges for such control loops. This study investigates the impact of using various Frequency-Linked Pricing (FLP)-based operating strategies under Availability-Based Tariff (ABT) on combined ALFC and AVR in a two-area interconnected power system with DFIGs. FLP-based operating strategies under ABT react to frequency changes through price signals, which can act faster compared to traditional AGC methods that rely on communication between control centers. This quicker response helps maintain frequency and voltage during load disturbances. Most of the earlier research focused mostly on using only a single type of FLP-based operating strategy under ABT within the ALFC loop, neglecting the combined ALFC-AVR interaction. This work addresses this gap by evaluating three FLP-based operating strategies under ABT along with the traditional approach for their impact on frequency and voltage under step load disturbance. Simulation studies conducted in MATLAB 2022a demonstrate the effectiveness of the proposed strategies, providing valuable insights for improving frequency and voltage control in modern power systems. The investigation reveals that the strategy that considers the difference between the actual and reference values of the unscheduled interchange (UI) charge and the marginal cost of generation to produce the control command outperforms the other strategies and restores the frequency and voltage to their nominal values.