Miracle-dz/UniFAR-FTUs huggingface dataset:
A Composite Anomaly Detection System for Data-Driven Power Plant Condition Monitoring

Data-driven condition monitoring is an essential function for power plant because of its potential to enhance asset longevity and reduce the operation and maintenance costs. This article explains the complicated relationship in multiplex power plant data as a mixture of temporal dependency and cross-variable association and proposes a composite anomaly detection system that incorporates the two data relationships on a probabilistic basis for more reliable power plant condition monitoring. It is able to dynamically capture the most significant relationship to develop more reliable normal condition interval, based on which the potential faults can be timely detected and the abnormal variable can be accurately identified. The proposed system was tested on a realistic thermal power plant. The testing results demonstrate its reliable condition monitoring and accurate anomaly detection performance, which necessitates the composite modeling of temporal dependency and cross-variable association in data-driven power plant condition monitoring.
ACROMEGALY: A CASE REPORT -

Acromegaly is a rare disease caused due to hyper secretion of growth hormone. Most of the cases of acromegaly are caused by pitutary adenoma which can be microadenoma or macroadenomas. These adenomas are never malignant, but can have significant morbidity and mortality. We report a 35 year old female patient presented herewith classical presentation of acromegaly with chief complain of weight gain, excessive sweating , widening of both hands and feet and was diagnosed as a case of acromegaly due to macroadenoma of pirtutary gland, on the basis of typical clinical features and hormonal parameters also radiological findings. Patient underwent transsphenoidal surgical resection of macroadenoma and recovered completely from the disease. Early recognition and diagnosis will help to avoid the complications of disease.
Redundant Hybrid Cable-Driven Robots: Modeling, Control, and Analysis

Serial and Cable-Driven Parallel Robots (CDPRs) are two types of robots that are widely used in industrial applications. Usually, the former offers high position accuracy at the cost of high motion inertia and small workspace envelope. The latter has a large workspace, low motion inertia, and high motion accelerations, but low accuracy. In this thesis, redundant Hybrid Cable-Driven Robots (HCDRs) are proposed to harness the strengths and benefits of serial and CDPRs. Although the study has been directed at warehousing applications, the developed techniques are general and can be applied to other applications. The main goal of this research is to develop integrated control systems to reduce vibrations and improve the position accuracy of HCDRs. For the proposed HCDRs, the research includes system modeling, redundancy resolution, optimization problem formulation, integrated control system development, and simulation and experimental validation. In this thesis, first, a generalized HCDR is proposed for the step-by-step derivation of a generic model, and it can be easily extended to any HCDRs. Then, based on an inplane configuration, three types of control architecture are proposed to reduce vibrations and improve the position accuracy of HCDR. Their performance is evaluated using several well-designed case studies. Furthermore, a stiffness optimization algorithm is developed to overcome the limitations of existing approaches. Decoupled system modeling is studied to reduce the complexity of HCDRs. Control design, simulations, and experiments are developed to validate the models and control strategies. Additionally, state estimation algorithms are proposed to overcome the inaccurate limitation of Inertial Measurement Unit (IMU). Based on these state observers, experiments are conducted in different cases to evaluate the control performance. An Underactuated Mobile Manipulator (UMM) is proposed to address the tracking and vibrationand balance-control problems. Out-of-plane system modeling, disturbance analysis, and model validation are also investigated. Besides, a simple but effective strategy is developed to solve the equilibrium point and balancing problem. Based on the dynamic model, two control architectures are proposed. Compared to other Model Predictive Control (MPC)-based control strategies, the proposed controllers require less effort to implement in practice. Simulations and experiments are also conducted to evaluate the model and control performance. Finally, redundancy resolution and disturbance rejection via torque optimization in HCDRs are proposed: joint-space Torque Optimization for Actuated Joints (TOAJ) and jointspace Torque Optimization for Actuated and Unactuated Joints (TOAUJ). Compared to
Review of DC Microgrid system with Various Power Quality Issues in “Real Time Operation of DC Microgrid Connected System”

This paper presents an overview of various microgrid architectures of DC microgrid and power quality issues including harmonic currents, inrush currents, bus fault, circulating currents, and grounding of the system. A microgrid utilizing a DC bus can avoid various power conversion steps, and hence minimize the loss incurred as compared to AC bus. The high frequency semiconductor devices with greater voltage blocking capacities are essential components of DC microgrid. With the advent of new power electronic devices and circuits, the practical implementation of DC microgrid still suffers from various power quality issues and device limitations. This paper investigates and compares the power loss and harmonics for different semiconductor devices by using MATLAB software. The experimental results indicate that SiC device implementation significantly increases energy efficiency and enhances microgrid power quality.
Trust in control: a trust model for power system network assessment

The question of whether a process variable transmitted from a device in the field to a power system control center is trustworthy is of high importance nowadays. Traditional bad data detection schemes have their limits in cases of elaborated cyberattacks and cascading failures in a system of systems such as a digitalized power system. This paper proposes a trust model designed for power system network assessment (PSNA). Different to other domains, where trust models already exist (e.g., OC-Trust for organic computing systems), the environment for PSNA is more centralized, and the focus lies on other facets than in organic computing due to the nature of the environment. Therefore, OC-Trust is tailored by categorizing its facets regarding their relevance for PSNA on the one hand. On the other hand, the trust model is extended to realize context-sensitive intersections of trust values. Furthermore, an example of an instantiation of the resulting PSNA-Trust model is given. Two security metrics and one credibility metric based on literature are presented as well as an equation for a context-sensitive intersection.
Model and Tools for Integrating IoT into Mixed Reality Environments: Towards a Virtual-Real Seamless Continuum

This paper introduces a new software model and new tools for managing indoor smart environments (smart home, smart building , smart factories, etc.) thanks to MR technologies. Our fully-integrated solution is mainly based on a software modelization of connected objects used to manage them independently from their actual nature: these objects can be simulated or real. Based on this model our goal is to create a continuum between a real smart environment and its 3D digital twin in order to simulate and manipulate it. Therefore, two kinds of tools are introduced to leverage this model. First, we introduce two complementary tools, an AR and a VR one, for the creation of the digital twin of a given smart environment. Secondly, we propose 3D interactions and dedicated metaphors for the creation of automation scenarios in the same VR application. These scenarios are then converted to a Petri-net based model that can be edited later by expert users. Adjusting the parameters of our model allows to navigate on the continuum in order to use the digital twin for simulation, deployment and real/virtual synchronization purposes. These different contributions and their benefits are illustrated thanks to the automation configuration of a room in our lab.
Matching theory as enabler of efficient spectrum management in 5G networks

This paper analyzes the spectrum trading problem in virtualized fifth generation networks to enhance the network performance with respect to the spectrum utilization. The problem is modeled as a many‐to‐many matching game with utility‐based preferences and determines the matching between mobile network operators and mobile virtual network operators. The two proposed versions of utility functions for each set aim at maximizing the satisfaction of both sets with conflicting interests and improving the overall spectrum efficiency. In the simulation evaluation, the proposed scheme is compared with three different schemes in terms of the system utility, individual, and pair matching satisfaction. We also investigate the scalability aspects, the strategy plan impact on the matching performance of our proposed scheme, and at the same time, we attempt to make appropriate assumptions closer to reality. Our proposed scheme shows much better performance than the other schemes achieving a quite high level of satisfaction for the matching result on both sets.
Boolean Techniques in Testing of Digital Circuits (Boolesche Techniken zum Testen von digitale Schaltkreise)

Currently, Very Large Scale Integrated (VLSI) circuits and the resulting digital systems are widely used in almost all areas of human’s life. To ensure the validity of these systems, error-free VLSI circuits are becoming more and more important. With the increasing complexity of VLSI circuits, the cost for the test on VLSI circuits has risen dramatically. So it is necessary to reduce the cost of test. This thesis proposes two approaches to achieve this goal. One approach, used before manufacture, is to improve the circuit structure for a better testability. The other one, used after manufacture, is to improve the test tool for a higher efficiency of fault detection. In this thesis, Binary Decision Diagrams (BDDs) are used to improve the structure of VLSI circuits for a better testability. It has been proved that Automatic Test Pattern Generation (ATPG) for BDD circuits under the Stuck At Fault Model (SAFM), the Path Delay Fault Model (PDFM) and the Bridging Fault Model (BFM) can be carried out in polynomial time. A new technique that adds a new input and an inverter to BDD circuits has been presented. Using this technique, the testability of circuits under SAFM, PDFM and BFM can be greatly improved. Especially, under SAFM and PDFM, 100% testable circuits can be generated. On the other hand, a technique based on Boolean Satisfiability (SAT) is proposed to improve the efficiency of test tools. A SAT-based ATPG algorithm suited for large industrial circuits with tri-state elements has been presented. It can generate test patterns for the faults that are aborted by classical algorithms. The combination of a classical TPG stage and the SAT-based TPG stage has been integrated into an industrial ATPG tool. Experimental results have demonstrated the quality and efficiency of this combination.
Automatic Leaderboard: Evaluation of Singing Quality Without a Standard Reference

Automatic evaluation of singing quality can be done with the help of a reference singing or the digital sheet music of the song. However, such a standard reference is not always available. In this article, we propose a framework to rank a large pool of singers according to their singing quality without any standard reference. We define musically motivated absolute measures based on pitch histogram, and relative measures based on inter-singer statistics to evaluate the quality of singing attributes such as intonation, and rhythm. The absolute measures evaluate the goodness of pitch histogram specific to a singer, while the relative measures use the similarity between singers in terms of pitch, rhythm, and timbre as an indicator of singing quality. With the relative measures, we formulate the concept of veracity or truth-finding for the ranking of singing quality. We successfully validate a self-organizing approach to rank-ordering a large pool of singers. The fusion of absolute and relative measures results in an average Spearman's rank correlation of 0.71 with human judgments in a 10-fold cross-validation experiment, which is close to the inter-judge correlation.
A feedback control approach for preventing system resource exhaustion caused by software aging

The dynamic behavior of software systems attracts widened attention through the phenomenon of software aging. Software aging is caused by runtime environment deterioration, such as the gradual loss of memory or CPU cycles. The dynamic behavior of aged software systems can be described by a set of evolving resource variables, including CPU usage, I/O bandwidth, available memory and the like. From this point of view, an aging software system can be analogous to a dynamic system. Control theory provides sound and rigorous mathematical principles to analyze dynamic systems and build controllers for them. This paper introduces control theory to analyze and build a control model and apply control techniques to an aged web server. First, we treated the software system as a black box, and conducted controlled experiments to build the relationship between input and output. Then, these input-output couples are used to build a control model via a system identification method. Finally, a PI (proportional-integral) controller is designed to adjust the aged state of the software system, and software rejuvenation techniques are customized to target the web server. Performance testing shows that our approach can accurately track the reference value set by the website administrator.
Orchestrating In-Band Data Plane Telemetry With Machine Learning

In-band network telemetry (INT) is an emerging network monitoring paradigm. By collecting low-level telemetry items in real time, INT can substantially enhance network-wide visibility - allowing, for example, timely detection problems such as micro-burst. Recent studies have focused on (i) developing INT mechanisms to increase network-wide visibility; and (ii) to design new monitoring solutions. However, little has been done to coordinate the process of collecting telemetry items in this new paradigm. This is particularly challenging because depending on which network telemetry items are collected, it might degrade network-wide visibility in terms of consistency/freshness. In this letter, we theoretically formalize the In-band Network Telemetry Orchestration Plan Problem and propose a machine learning based orchestration model. Results show that our approach outperforms state-of-the-art heuristics by up a factor of 8x with respect to the number of network anomalies identified, for instance.
Modified particle swarm optimization with novel population initialization

Abstract Particle swarm optimization (PSO) algorithm is such a simple, easily understood and realizable concept that it has been developing rapidly and has been applied widely since it was introduced. This paper proposes a modified particle swarm optimization(MPSO) algorithm with novel initializing particle swarms to improve the performance of the standard PSO. It is tested with a benchmark function compared with the standard PSO. Experimental results indicate that MPSO performs much better than the standard PSO both in terms of the speed of solutions and robustness.
Diagnosis and prognosis for subsequent pregnancy

Abstracr. Laparoscopy for the investigation of infertility was performed in 433 women. The rate of pathologic findings was 70%. No fewer than 55% of patients with no history of gynecologic disease and normal hysterosalpingography (HSG) were found to have pathologic findings. When comparing the patency of the Fallopian tubes at HSG and laparoscopy the overall agreement was 71 %. Sixty-eight of the patients with no other cause for infertility had the abdominal tuba1 ostia catheterized at laparoscopy. In 20 of these, small adhesions that could interfere with ovum pickup were found and divided. The pregnancy rate in this group was 70%, which is significantly higher than the pregnancy rate of 44% in 59 otherwise normal patients who did not have their tubes catheterized. The subsequent pregnancy rate for patients with different findings at laparoscopy is described. The role of HSG compared with laparoscopy is discussed. It is concluded that laparoscopy should be performed in all patients of female infertility, with the exception of those who have bilateral hydrosalpinges without patency on HSG. These patients are offered laparotomy for salpingostomy without laparoscopy. The catheterization of the Fallopian tubes at laparoscopy is a new method to find an easily treatable cause of infertility.
How steep is that street?: Mapping ‘real’ pedestrian catchments by adding elevation to street networks

Objective: This paper develops a way of incorporating steepness into ‘ped-shed’ analyses to provide a more realistic view of urban walkability. The methodology is tested on the street network in Milton, north Glasgow, but presented such that it could be applied more widely. Method: Elevation data is added to existing and proposed street networks in Milton which enables a calculation of average walking speed per segment to be made taking into account steepness. The networks are then run through ArcGIS’s Network Analysis extension, using time (adjusted for uphill and downhill slope) as impedance. Results: ‘Real’ walkable 10-minute roundtrip catchments around neighbourhood centres are compared with 2D catchments, for existing and proposed street networks. As expected, the ‘real’ ‘ped-shed’ is smaller than the 2D ‘ped-shed’ for both networks, although the walkability of the more connected, proposed street network is less affected by steepness, most likely as a result of greater choice in route selection. Discussion: This research builds on existing established practice in walkability analysis, incorporating the often-overlooked steepness variable as a key statistical element of the walking experience. It also prompts a discussion on other factors which may affect walkability and could be included in a more sophisticated walkability index.
Mininet: a versatile tool for emulation and prototyping of Software Defined Networking

Software Defined Networks is a new paradigm in data networks, which in recent years is being focus of study and research because of the different advantages regarding the operation of conventional networks. Software Defined Networks separates the data plane from the control plane of a network device, allowing control, monitoring and management of a network from a central node or controller, which promises to simplify network management. It also includes innovation through its programmability. Given the fact that Software Defined Networks is a subject of current research, and given the importance of taking the tools to simulate or emulate network devices to test the solutions proposed by researchers and new features; this paper presents primarily a general conceptualization of Software Defined Networks, then describes different tools for simulation and emulation current used, and finally it focuses on the study of emulation tool called Mininet, showing installation modes, creating topologies, network controllers and main features revealing his versatility in this new paradigm prototyping. Descriptors Mininet, OpenFlow, Software Defined Networking.
Evaluation of the Utility of Knowledge Representation Languages in Domain Ontologies Using the MLP Method

The article covers the issues of knowledge representation languages in domain ontologies: LOOM, OCML, OWL, FLogic, SHOE, RDF(S), Ontolingua, and OWL2. The aim of the undertaken and described studies is the evaluation of utility of these languages using the MLP method and the ability to improve the properties of the selected language, which has a high applicability in the construction of the domain ontologies. The result is a ranking of languages based on the assessment in the form of MLP utility functions with preference attributes specified with the AHP method and also is to show the possibility of simulation and improvement of attribute properties of the selected language.
New method of bearing fault diagnosis based on mmemd and DE_ELM

: A new method of bearing fault diagnosis based on multi-masking empirical mode decomposition (MMEMD) and extreme machine learning optimised by differential evolution algorithm (DE_ELM) is proposed in this study. MMEMD is an improvement of empirical mode decomposition (EMD). By adding masking signals to the signals to be decomposed in different levels, MMEMD can restrain low-frequency components from mixing in high-frequency components effectively in the sifting process and then suppress the mode mixing. Differential evolution algorithm is applied to determine the parameters of ELM for improving the classification accuracy. The four parameters are determined at one time by uniformly coded as the individuals of the differential evolution algorithm. To achieve the bearing fault diagnosis, the fault signals are first decomposed into different intrinsic mode functions (IMFs) and the sample entropy of each IMF was calculated as the fault feature. Then the fault feature was divided into training set and testing set. Input the training set to the DE_ELM to obtain the fault classification model. Finally, the testing set was put into the model for fault diagnosis. The experiment and wind turbine bearing fault diagnosis results show that the method could identify the different bearing faults with high reliability and accuracy.
VIRTEL – Teaching hospitality with a virtual hotel e-simulation

The conference was organized by the Asia-Pacific Educational Research Association, the Asia-Pacific Centre for Education Leadership and School Quality (APCELSQ) of The Hong Kong Institute of Education and The Hong Kong Education Research Association. The theme was "Educational research, policy, and practice in an era of globalization : the Asia-Pacific perspectives and beyond. "
HEALTHY EATING HABITS FOR A BETTER LIFE

The purpose of this study was to analyze the main characteristics of dietary behaviour among young people and their perception of what a healthy diet means. The study is based on an online alimentation questionnaire with 42 questions referring the daily eating habits. The participants (students) that answered and submitted these questionnaires have the age between 18 and 35 years. The results of our study indicate that a high percentage of the participants consider breakfast important and they usually eat cereals and eggs. Based on their answers, a list of the favourite vegetables and fruits was obtained. This study revealed that most people involved have a relatively healthy eating style and a good understanding of the importance of the daily diet and exercises for the state of health.