Current System Research Articles

KSTAR Grounding System and the Operation Result of the Ground Current Detection System

KSTAR Grounding System and the Operation Result of the Ground Current Detection System

Design and research of grounding current monitoring device for converter transformer core and clamp

The effective monitoring of the grounding current of the core and clamp of the converter transformer can prevent partial overheating, gassing of oil, and other abnormal conditions caused by the grounding fault of the core and clamp, ensuring the safety and stability of the power system. At present, the main detection and diagnosis method is to use the clamp current meter to detect the effective value of the grounding current. However, due to the larger capacity, higher voltage level, more complex structure and electromagnetic field distribution of the converter transformer, there is no clear standard and detection diagnosis method. Firstly, this paper analyzes the causes of grounding current and related calculations. And the design of the monitoring system for the grounding current of the converter transformer core and clamp design of the hardware system for the grounding current of the converter transformer is studied and the design process of the main circuit is given. Secondly, aiming at the problem of amplitude measurement deviation and non-integer frequency of harmonic generated by the grounding current of the core and clamp, a harmonic current detection and analysis scheme based on four Blackman-harris algorithms and the specific design process are proposed. Third, a diagnostic method based on probabilistic neural network is proposed, and a software platform is built to display and diagnose the grounding current state according to it. Finally, a prototype was manufactured and experimental study was carried out to verify the correctness of the proposed scheme.

Thickness Measurement of Thermal Barrier Coating Based on Mutual Inductance of Eddy Current System

Thickness Measurement of Thermal Barrier Coating Based on Mutual Inductance of Eddy Current System

Validation and Implementation in the Cloud of Cell-Level Models of a Digital Twin Simulation Platform

The monitoring and modelling of the Li-Ion Batteries behaviour is still a major technical challenge due to the non-linearities and coupled phenomena that determine their operation. These Li-Ion Batteries can consist of thousands of cells with series/parallel connections, which suffer from operating and degradation deviations. The pursuit of new, increasingly intelligent and heavier state estimation algorithms requires a significant amount of data and computational power, which can be challenging to deploy in current Battery Management System solutions. To solve this problem, this paper proposes a Digital Twin Simulation Platform that considers all the individual cells based on the Cloud to extend the computational power and data storage capacity. This work presents validated cell models, a module-level modelling approach, and an experimental validation platform is suggested. In addition, the first results obtained when implementing the Digital Twin Simulation Platform in the Cloud are presented.

Semi-Supervised Learning for Multi-Label Cardiovascular Diseases Prediction: A Multi-Dataset Study.

Electrocardiography (ECG) is a non-invasive tool for predicting cardiovascular diseases (CVDs). Current ECG-based diagnosis systems show promising performance owing to the rapid development of deep learning techniques. However, the label scarcity problem, the co-occurrence of multiple CVDs and the poor performance on unseen datasets greatly hinder the widespread application of deep learning-based models. Addressing them in a unified framework remains a significant challenge. To this end, we propose a multi-label semi-supervised model (ECGMatch) to recognize multiple CVDs simultaneously with limited supervision. In the ECGMatch, an ECGAugment module is developed for weak and strong ECG data augmentation, which generates diverse samples for model training. Subsequently, a hyperparameter-efficient framework with neighbor agreement modeling and knowledge distillation is designed for pseudo-label generation and refinement, which mitigates the label scarcity problem. Finally, a label correlation alignment module is proposed to capture the co-occurrence information of different CVDs within labeled samples and propagate this information to unlabeled samples. Extensive experiments on four datasets and three protocols demonstrate the effectiveness and stability of the proposed model, especially on unseen datasets. As such, this model can pave the way for diagnostic systems that achieve robust performance on multi-label CVDs prediction with limited supervision.

Pharmacy practice and First Peoples health equity: a scoping review protocol.

The objective of this review is to examine and describe global pharmacy practice strategies and interventions designed to achieve health equity for First Peoples. Access to medicines and quality use of medicines is critical to achieving health equity for First Peoples. Pharmacists are uniquely placed to lead the charge in transforming current health systems, reducing health disparities, and bolstering the movement toward health equity. Global studies describing pharmacy practice strategies and interventions designed to achieve health equity for First Peoples will be considered for inclusion in the review. Studies relating to all areas of pharmacy practice, including community and clinical pharmacy, social, administrative, pharmaceutical sciences, practice, teaching, research, advocacy, or service relevant to the review's objective will also be considered for inclusion. The types of studies to be included are qualitative, quantitative, and mixed methods systematic reviews, scoping reviews, literature reviews, and gray literature. This review will be conducted in accordance with JBI methodology for scoping reviews. Embase, MEDLINE, Scopus, CINAHL, and gray literature sources will be searched from 1998 to the present. Titles, abstracts, and full texts will be screened against the inclusion criteria. Strategies and interventions identified in the included reviews will be mapped to a published framework, outlining actionable strategies for pharmacy practice inclusion in sustainable efforts to achieve health equity. Qualitative content analysis and descriptive statistics will be utilized with data presented in tables, accompanied by a narrative. Open Science Framework osf.io/qa64b.

Multidecadal Phase Changes in the Thermodynamic State of the System: Ocean–Atmosphere–Continent

The present-day climate (the recent 100–150 years) obviously constitutes the structure of a global intra-system rhythmic process with an individual rhythm of about 60 years. In turn, each of the rhythms is presented by the two climate phases of about 25–35 years characterized by qualitative differences: one phase is relatively continental, while the other is humid. Globality and quasi-synchronism of environmental changes are accompanied by planetary structures: the Global Atmospheric Oscillation (GAO) in the atmosphere and the Multidecadal Oscillation of the Heat content in the Ocean (MOHO) discovered relatively recently. Unexpected and rapid qualitative phase changes in the climate, which first focused attention in the mid-1970s of the last century, were titled “climate shifts”. The revealed features of the present-day climate are of exceptional scientific and practical interest and deserve the development of methods for predicting the timing of the forthcoming climate shift. Arising unexpectedly and accompanied by rapid significant changes, these shifts identified the problem of understanding the nature and establishing the processes and mechanisms causing them. First of all, of interest are phase changes in the thermodynamic state of the climate system components: the ocean, atmosphere, and continents. As a result of the World Ocean (WO) thermohydrodynamics numerical modelling, it is shown that MOHO is localized in the layer of the main thermocline, where the most important elements of the WO circulation are located. The performed study based on observational data allows us to conclude that, during the phase of the WO thermal discharge (1975–1999), the two key systems of currents, the Kuroshio and the Gulf Stream, were under similar thermodynamic conditions.

Data-Driven Modeling and Operation Optimization With Inherent Feature Extraction for Complex Industrial Processes

In response to the tenets of Industry 4.0, operation optimization in industrial processes has become a significant research topic. However, the uncertainties prevailing in the process pose challenges to production operations, especially the feedstock properties. In this work, the operation optimization study is performed on a distillation unit (DU), a typical plant in the industrial process. To enhance production performance, a modeling and operation optimization strategy based on feedstock property and production features is presented. One of the difficulties is how to uncover features from high-dimensional and imperfect data, where imperfect data refers to product quality data that is unavailable online. In the strategy, we inject the inherent characteristic of the process into the data-driven method to extract the feedstock property in a data-based and knowledge-oriented manner. Further, optimal feature representation and process modeling can be achieved by customizing the network structure. The operation optimization problem is formulated to adjust the top temperature of the distillation column (TTDC) to achieve satisfactory production under varying feedstock properties. Experimental results illustrate that the process model based on feedstock property and production features (PM-FP-PF) can better fit the physical process mechanism even based on incomplete information in industrial data. Industrial experiments have shown the proposed strategy has advanced generalization ability to the different feedstock properties. The proposed operation optimization strategy (OOS) improves the product qualification rate and has broad application prospects in industrial processes with similar features. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Industrial processes suffer from a variety of disturbances that interrupt the smooth operation of the system, such as varying feedstock properties. How to deal with them is the key to improve the product qualification rate. In this work, we propose a data-driven modeling and operation optimization framework to improve product quality under varying feedstock properties. The dynamic variation characteristics of the feedstock properties can be obtained by analyzing the physical properties of the production unit. This is used as a basis for representing and extracting feedstock properties in a data-driven way. Further, a process model based on feedstock property and production features (PM-FP-PF) is built to predict product quality. An operation optimization strategy with production capacity consideration is established. It can determine the optimal operation action required by the current system, mitigating the uncertainty of feedstock property. This operation optimization system has been applied to a distillation unit in the hydrofining process. The application results show that the process model achieves satisfactory estimation accuracy, and the operation optimization strategy has improved production performance.

Design of Real Time Intelligent System for Women Safety

Background: Even the technology has advanced a lot in these modern times, women's safety is still a massive issue. Women are not safe anywhere. Their safety is an essential and decisive issue in today’s world. They face extreme troubles when they are alone, travelling on lonely roads and at workplace. Education is essential for everyone and especially for women. It is a powerful weapon to end poverty. Government and NGOs have made many rules and regulations but the crime against women has not ended. There are many instances of harassment, abuse, eve-teasing, kidnapping, domestic violence and rape. Women are working equally as men but still their safety is a big challenge. With the boom in the IT and corporate sector, many women are now working and even during nightshifts. Objectives: A Real Time Intelligent System for Women Safety has been proposed to overcome these limitations. Methods: In this paper, many women's safety systems that were proposed or developed have been discussed. Major drawbacks in the current system, devices or applications have been highlighted. Latest technologies like machine learning, artificial intelligence or data science have been ignored. Results: The proposed system will be highly sophisticated with the latest techniques and women will feel safe in all situations. It will predict the safe route while moving alone. Conclusion: Moreover, it will record the evidence and will also suggest the rights of women.

A Novel Lightweight Navigation System for Oral and Maxillofacial Surgery Using an External Curved Self-Identifying Checkerboard

This paper presents a novel lightweight navigation system for oral and maxillofacial surgery (OMS). An external curved checkerboard with self-identifying markers is set as the reference object around the surgical scene. A customized oral clip with a micro camera is designed for oral localization by tracking the external checkerboard. Similarly, the dental handpiece is also equipped with a micro camera, which can be localized like the clip. The spatial model of the markers is provided by binocular stereoscopic reconstruction. A front surface mirror is taken up for the registration between the oral cavity and the camera on the clip. The pivot calibration of the dental handpiece is accomplished by our proposed calibration method. We set up an experimental group and a control group for evaluation. The surgical tools of the experimental group were approximately 30% lighter, 35% less bulky, and 90% cheaper than those of the control group. Our system yielded the comprehensive navigation accuracy of 0.92 mm whereas the accuracy of the control group was 0.87 mm. Results revealed that our system can achieve similar accuracy compared with a prevailing system at a lighter weight, a more compact volume, and a lower cost. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The motivation of this work is to reduce the burden on patients and surgeons during OMS. Current commercial navigation systems are still limited by the burden of extra cumbersome fiducial markers and high hardware costs. Their high accuracy benefits from the large size of fiducial markers. To take full advantage of the camera’s localization effect, we propose the concept of “marker-camera inverse projection”, i.e., reversing the roles of the camera and the markers. In this way, cameras on surgical tools detect more points with a more uniform distribution. Our proposed system achieves a decent balance between navigation accuracy and hardware cost, which facilitates the development of surgical tools to be lighter and more economical, and involves tremendous potential for commercialization.

Implementing Blockchain in the Management of Organ Donation and Transplantation

Abstract: Current organ donation and transplantation systems suffer from vulnerabilities to scams and lack of transparency, hindering trust and efficiency. This proposal introduces a groundbreaking blockchain-based solution that combats both issues. By leveraging the blockchain's security and immutability, the system detects and predicts potential scams through AI analysis of realtime data, notifying authorities immediately. Additionally, it strengthens security and trust by ensuring secure participant identities, controlled access to sensitive information, and automated processes via smart contracts. This not only reduces the risk of illegal activity but also fosters fairness and participation, ultimately transforming the system into a life-saving model of transparency and trust. But that's not all. Blockchain securely stores identities, encrypts sensitive information, and automates tasks with smart contracts, eliminating the human error and shady dealings that plague the current system. This creates a web of trust, empowering donors, recipients, and medical professionals alike. The result? A system that's not just secure, but fair and efficient, saving lives and restoring faith in this vital field. It's time to transplant trust into organ donation, and blockchain is the fertile ground for its growth

Air Writing Word Recognition and Translation

Abstract: Project develops a system that can recognize the air-written words in free space, and then classify the recognized word. Air-writing is the new way of writing the linguistic words in free area using hand or finger movements. This project could be a combination of computer vision hand chase and handwriting recognition machine learning. The air writing recognition system uses the web camera of a pc to trace word written within the air by the user and then uses a convolutional neural network to classify the word into one of the possible classes. Several current systems use advanced and high-priced chase setups to realize gesture recognition, however, we tend to get to form a system that may attain a similar work with a far cheaper setup.

A review of wastewater-based epidemiology for antimicrobial resistance surveillance

Antimicrobial resistance (AMR) is recognized as one of the most serious threats to public health. Unparalleled population growth and accelerated rates of AMR emergence and dissemination have resulted in both novel resistance in pathogenic organisms and the re-appearance of infections that were formerly under control. Consequently, this has led to an increased quantity of infectious diseases. One of the main drivers of antimicrobial overuse is inappropriate prescribing in human and veterinary medicine. The ability to rapidly survey the spread of antimicrobial resistance within human populations is key for its prevention, intervention, and control. However, many constraints are present for current clinical surveillance systems and their capacity to determine AMR dynamics in the microbiome of healthy individuals as well as in clinical pathogens causing infections. Wastewater-based epidemiology (WBE) is an emergent technique that has the capacity to act as a supplementary measure for current infectious disease surveillance systems and as an early warning system for infectious disease outbreaks. The development of disease outbreaks to the community level can be monitored in real time through the analysis of population pooled wastewater. This review provides an introduction to using wastewater-based epidemiology to monitor AMR bacteria, as well as an overview of wastewater-based epidemiology and its components.

Light-Mediated Polymerization Catalyzed by Carbon Nanomaterials.

Carbon nanomaterials, specifically carbon dots and carbon nitrides, play a crucial role as heterogeneous photoinitiators in both radical and cationic polymerization processes. These recently introduced materials offer promising solutions to the limitations of current homogeneous systems, presenting a novel approach to photopolymerization. This review highlights the preparation and photocatalytic performance of these nanomaterials, emphasizing their application in various polymerization techniques, including photoinduced i) free radical, ii) RAFT, iii) ATRP, and iv) cationic photopolymerization. Additionally, it discusses their potential in addressing contemporary challenges and explores prospects in this field. Moreover, carbon nitrides, in particular, exhibit exceptional oxygen tolerance, underscoring their significance in radical polymerization processes and allowing their applications such as 3D printing, surface modification of coatings, and hydrogel engineering.

FedAGAT: Real-time traffic flow prediction based on federated community and adaptive graph attention network

Predicting traffic flow is vital for optimizing intelligent transportation systems (ITS) and reducing congestion by forecasting traffic patterns accurately. However, current centralized TFP systems have limitations, e.g., slow training, high communication costs, and privacy concerns. To address these challenges, this study proposes FedAGAT, a system for short-term traffic flow prediction (TFP) based on federated community and adaptive spatial-temporal graph attention networks (AGAT). The FedAGAT system allows for local data processing and sharing only model updates with the server. This enables real-time, scalable, and secure TFP - essential capabilities for efficient ITS. AGAT is employed to capture intricate spatial-temporal dependencies in traffic flow, while federated learning facilitates decentralized learning, enhancing privacy. The FedAGAT prediction process involves four steps: dividing the local subnetwork using spectral community detection, locally training based on global parameters, uploading updated parameters, and creating a global model prediction based on the aggregated parameters. To evaluate the performance of FedAGAT, two real-world traffic datasets were utilized for benchmarking against seven statistical and deep learning models. Results demonstrate that FedAGAT provides relatively higher accuracy for short- and mid-term forecasting horizons. Moreover, FedAGAT predictions closely match real traffic flow values, and the overall performance is comparable to a global model, while requiring less time.

Analisis Sistem Informasi Akuntansi Penggajian Dalam Upaya Mendukung Pengambilan Keputusan Pada Rumah Sakit Umum Haji Medan

Current economic developments have been encouragingcompany growth to enlarge and expand spacescope of business. Likewise, current information systems havemakes it easier for companies to deal with problems that may occur in the payroll process. Objective This research is to find out how the system is implemented payroll accounting at the Medan Haji General Hospital and for find out whether the payroll accounting system is implemented by Companies can help management in decision making towards salary. This type of research is a type of qualitative research with a descriptive approach that uses primary data such as results of interviews with the company and secondary parties such as structureorganization. Data collection techniques were carried out using techniques observation, interviews, and literature, data analysis methods used is a descriptive method. This research was conducted in Medan Haji General Hospital which is located on Jl. Hajj Hospital – Estate terrain and research time starts in January 2024 until the completion of this thesis. The results of this research show that the accounting system implemented by the Hajj General Hospital to handle payroll has been quite adequate in helping company management in making decisions regarding salaries, p This is good because the company employs employees from a wide range levels and in very large numbers and provide rewards for their services in the form of salaries, so the company must have effective accounting system.

Infectious Diseases - a new specialty for postgraduate training in Germany

Medicine in Germany is currently facing major structural and economic challenges. Infectious Diseases, with the recent introduction of a new specialty in "Internal Medicine and Infectious Diseases" and with the existing additional training for almost all specializations, will make an important contribution to overcoming these challenges. Expertise in infectious diseases has to be very broad and requires high interdisciplinarity, which makes infectious diseases an attractive and demanding specialty. The complex fundamentals of infectious diseases must now be quickly conveyed to as many physicians as possible in a short period of time, as part of their specialization or as additional training. Until this is achieved, transitional solutions will be necessary for some time. The adaptation of the current billing and reimbursement system for infectious diseases services and improved intersectoral cooperation are of the utmost importance for the further development of the specialty.

Transcendental philosophy and logic diagrams

AbstractLogic diagrams have seen a resurgence in their application in a range of fields, including logic, biology, media science, computer science and philosophy. Consequently, understanding the history and philosophy of these diagrams has become crucial. As many current diagrammatic systems in logic are based on ideas that originated in the 18th and 19th centuries, it is important to consider what motivated the use of logic diagrams in the past and whether these reasons are still valid today. This paper proposes that transcendental philosophy was a key inspiration for the development of logic diagrams and that such diagrams can be employed in transcendental arguments, even after the linguistic turn.

Safety of Perception Systems for Automated Driving: A Case Study on Apollo

The automotive industry is now known for its software-intensive and safety-critical nature. The industry is on a path to the holy grail of completely automating driving, starting from relatively simple operational areas like highways. One of the most challenging, evolving, and essential parts of automated driving is the software that enables understanding of surroundings and the vehicle’s own as well as surrounding objects’ relative position, otherwise known as the perception system. Current generation perception systems are formed by a combination of traditional software and machine learning-related software. With automated driving systems transitioning from research to production, it is imperative to assess their safety. We assess the safety of Apollo, the most popular open-source automotive software, at the design level for its use on a Dutch highway. We identified 58 safety requirements, 38 of which are found to be fulfilled at the design level. We observe that all requirements relating to traditional software are fulfilled, while most requirements specific to machine learning systems are not. This study unveils issues that need immediate attention; and directions for future research to make automated driving safe.

Early Validation and Verification of System Behaviour in Model-based Systems Engineering: A Systematic Literature Review

In the Systems Engineering (SE) domain there has been a paradigm shift from document-based to model-based system development artefacts; in fact, new methodologies are emerging to meet the increasing complexity of current systems and the corresponding growing need of digital workflows. In this regard, Model-Based Systems Engineering (MBSE) is considered as a key enabler by many central players of the SE community. MBSE has reached an adequate level of maturity, and there exist documented success stories in its adoption in industry. In particular, one significant benefit of utilising MBSE when compared to the traditional manual and document-centric workflows is that models are available from early phases of systems development; these enable a multitude of analyses prior any implementation effort together with other relevant capabilities, like the automation of development tasks. Nonetheless, it is noticeable there is a lack of a common understanding for how formal analyses for the verification and validation (V&amp;V) of systems behaviour, specifically in the early phases of development, could be placed in an MBSE setting. In this article, we report on the planning, execution, and results of a systematic literature review regarding the early V&amp;V of systems behaviour in the context of model-based systems engineering. The review aims to provide a structured representation of the state of the art with respect to motivations, proposed solutions, and limitations. From an initial set of potentially relevant 701 peer-reviewed publications we selected 149 primary studies, which we analysed according to a rigorous data extraction, analysis, and synthesis process. Based on our results, early V&amp;V has usually the goal of checking the quality of a system design to avoid discovering flaws when parts are being concretely realised; SysML is a de facto standard for describing the system under study, while the solutions for the analyses tend to be varied; also V&amp;V analyses tend to target varied properties with a slight predominance of functional concerns, and following the variation mentioned so far the proposed solutions are largely context specific; the proposed approaches are usually presented without explicit limitations, while when limitations are discussed, readiness of the solutions, handling of analyses simplifications/assumptions, and languages/tools integration are among the most frequently mentioned issues. Based on the survey results and the standard SE practices, we discuss how the current state-of-the-art MBSE supports early V&amp;V of systems behaviour with a special focus on industrial adoption and identify relevant challenges to be researched further.