bluuebunny/crossref_metadata_2025_split huggingface dataset:
Fantôme en héritage : du morcellement au lien
Authors: Rouchy Jean-Claude 
Published: March 2024

Fantasmas en herencia : de la fragmentación al vínculo

La fragmentación, la ausencia de vínculo, son unas de la principales indicaciones para un anàlisis de grupo. En él se manifiestan y pueden elaborarse : la ausencia de mentalización, la actuación, la desmetaforización y la objetivación.
En el origen hay un trauma, lo mas a menudo : trauma vivido por el sujeto o subsiguiente a una transmisión intergeneracional. La transmisión de secretos de famila, a escondidas del sujeto, descubiertos por Nicolas Abraham y Maria Torok plantean sin embargo problemas particulars.
La censura familiar concierne historias inconfesables de incesto, violación, homicidio, suicidio o cualquier otra tara que no debe ser conocida por la posteridad. Vienen a atormentar a la descendencia bajo forma de síntomas, de somatizaciones, de delirios, a veces a varias generaciones de distancia : fantasmas o criptas, «testimonios de la existencia de un muerto enterrado en el otro». Estos índices no pueden ser reconocidos mas que después de un tiempo de escucha. Conducirían mas bien a una indicación de psicoanàlisis que de anàlisis de grupo, pero al principio, los síntomas son los mismos.
¿ Cómo se manifesta todo esto en un grupo analítico, cômo se elabora, cómo se trata ? Una vineta clinica servira de apoyo a estas cuestiones.
Adversarial Camera Patch: An Effective and Robust Physical-World Attack on Object Detectors
Authors: Tiliwalidi Kalibinuer , Hui Bei , Hu Chengyin , Ge Jingjing 
Published: March 2024

Physical adversarial attacks present a novel and growing challenge in cybersecurity, especially for systems reliant on physical inputs for Deep Neural Networks (DNNs), such as those found in Internet of Things (IoT) devices. They are vulnerable to physical adversarial attacks where real-world objects or environments are manipulated to mislead DNNs, thereby threatening the operational integrity and security of IoT devices. The camera-based attacks are one of the most practical adversarial attacks, which are easy to implement and more robust than all the other attack methods, and pose a big threat to the security of IoT. This paper proposes Adversarial Camera Patch (ADCP), a novel approach that employs a single-camera patch to launch robust physical adversarial attacks against object detectors. ADCP optimizes the physical parameters of the camera patch using Particle Swarm Optimization (PSO) to identify the most adversarial configuration. The optimized camera patch is then attached to the lens to generate stealthy and robust adversarial samples physically. The effectiveness of the proposed approach is validated through ablation experiments in a digital environment, with experimental results demonstrating its effectiveness even under worst-case scenarios (minimal width, maximum transparency). Notably, ADCP exhibits higher robustness in both digital and physical domains compared to the baseline. Given the simplicity, robustness, and stealthiness of ADCP, we advocate for attention towards the ADCP framework as it offers a means to achieve streamlined, robust, and stealthy physical attacks. Our adversarial attacks pose new challenges and requirements for cybersecurity.
STRUKTUR KOMUNITAS EKOSISTEM PADANG LAMUN DI PERAIRAN DESA BORGO KECAMATAN BELANG KABUPATEN MINAHASA TENGGARA (Community Structure of Ecosystem Seagrass Beds in Water of Borgo Village, Belang District, Southeast Minahasa Regency)
Authors: Losung Agung J. , Paruntu Carolus P. , Wagey Billy Th. , Roeroe Kakaskasen A. , Losung Fitje , Sambali Hariyani 
Published: March 2024

This research was conducted in the coastal waters of Borgo Village, Belang District, SoutheastMinahasa Regency using the line transect method adopted by the Seagrass Watch method. This studyaims to determine the community structure and physical-chemical environmental factors of seagrassbeds in the waters of Borgo Village, Belang District, Southeast Minahasa Regency. The results of thisstudy obtained 3 types of seagrass identified in the waters of Borgo Village, namely: Enhalus acoroides,Thalassia hemprichii, and Syringodium isoetifolium, with an average value of species density belongingto scale 2 with a density level of 25-75 ind/m2 which is included in sparse density conditions (11.99individuals/m2); the average value of relative concealment is classified as less rich/less healthy(33.34%); the species index is included in the low category, namely H'<1 (0.68); the uniformity index isincluded in the large/high category (0.33) the dominance index is included in the medium category(0.75). The parameter value of pH 8.00 is in the range of quality standard values while the temperatureof 32.00° C, brightness of 0.29 m, and salinity of 29.98 o/oo are not in the range of quality standardvalues.Keywords: Seagrass Community Structure, Species Composition, Borgo Village Waters
ABSTRAKPenelitian ini dilaksanakan di wilayah pesisir perairan Desa Borgo, Kecamatan Belang,Kabupaten Minahasa Tenggara dengan menggunakan metode metode line transect yang diadopsidengan metode SeagrassWatch. Penelitian ini bertujuan untuk mengetahui struktur komunitas danfaktor lingkungan fisika-kimia padang lamun di perairan Desa Borgo, Kecamatan Belang, KabupatenMinahasa Tenggara. Hasil penelitian ini memperoleh 3 jenis lamun yang terindentifikasi di perairanDesa Borgo yaitu: Enhalus acoroides, Thalassia hemprichii dan Syringodium isoetifolium, dengan nilairata-rata kerapatan jenis tergolong dalam skala 2 dengan tingkat kerapatan 25-75 ind/m2 yangtermasuk dalam kondisi kerapatan jarang (11,99 individu/m2); nilai rata-rata penutupan relatif jenistergolong kurang kaya/kurang sehat (33,34%); indeks keanekaragaman jenis termasuk dalam kategorirendah yaitu H’<1 (0,68); indeks keseragaman termasuk dalam kategori besar/tinggi (0,33) indeksdominansi termasuk dalam kategori sedang (0,75). Nilai parameter pH 8,00 berada pada kisaran nilaibaku mutu sedangkan suhu 32,00° C, kecerahan 0,29 m dan salinitas 29,98 o/oo tidak berada padakisaran nilai baku mutu.Kata kunci: Struktur Komunitas Lamun, Komposisi Jenis, Perairan Desa Borgo
Commercialization of Prognostics Systems Leveraging Commercial Off- The-Shelf Instrumentation, Analysis, and Data Base Technologies
Authors: Johnson Preston 
Published: March 2024

There are many facets of a prognostics and health management system. Facets include data collection systems that monitor machine parameters; signal processing facilities that sort, analyze and extract features from collected data; pattern matching algorithms that work to identify machine degradation modes; database systems that organize, trend, compare and report information; communications that synchronize prognostic system information with business functions including plant operations; and finally visualization features that allow interested personnel the ability to view data, reports, and information from within the intranet or across the internet.A prognostic system includes all of these facets, with details of each varying to match specific needs of specific machinery. To profitably commercialize a prognostic system, a generic yet flexible framework is adopted which allows customization of individual facets. Customization of one facet does not materially impact another. This paper describes the framework, and provides case studies of successful implementation.
Cost Comparison of Maintenance Policies
Authors: Minh Duc Le , Cher Ming Tan 
Published: March 2024

Maintenance is crucial to all repairable engineering systems as they will degrade and fail. The cost of maintenance for a manufacturing plant can occupy up to 30% of the total operating cost. If maintenance is not scheduled properly, unexpected equipment failure can induce significant cost due to reduced productivity and sub-standard products produced, both of which may result in customer penalty. Various maintenance policies have been proposed in the past. Among the various policies, age-dependent and periodic maintenances are the common policies employed in industries. Recently, predictive maintenance or condition based maintenance policies are also proposed owing to the advancement in the sensor technology. In this work, we compare the age-dependent and periodic maintenance policies as well as the predictive maintenance policies from the perspective of cost using Markov multi-state maintenance modeling and Monte Carlo simulation. To be realistic, imperfect maintenance is included, and both the sequential and continuous inspections are considered and compared.
Online Prediction of Battery Discharge and Estimation of Parasitic Loads for an Electric Aircraft
Authors: Bole Brian , Daigle Matthew , Gorospe George 
Published: March 2024

Predicting whether or not vehicle batteries contain sufficient charge to support operations over the remainder of a given flight plan is critical for electric aircraft. This paper describes an approach for identifying upper and lower uncertainty bounds on predictions that aircraft batteries will continue to meet output power and voltage requirements over the remainder of a flight plan. Battery discharge prediction is considered here in terms of the following components; (i) online battery state of charge estimation; (ii) prediction of future battery power demand as a function of an aircraft flight plan; (iii) online estimation of additional parasitic battery loads; and finally, (iv) estimation of flight plan safety. Substantial uncertainty is considered to be an irremovable part of the battery discharge prediction problem. However, highconfidence estimates of flight plan safety or lack of safety are shown to be generated from even highly uncertain prognostic predictions.
Ballast Degradation Modeling for Turnouts based on Track Recording Car Data
Authors: Asadzadeh Seyed Mohammad , Galeazzi Roberto , Hovad Emil , Andersen JF , Thyregod Camilla , Rodrigues Andre 
Published: March 2024

Turnouts play a central role in the railway infrastructure since they enable increased network capacity and allow for minimal impact of train delays. Their performance is of paramount importance for infrastructure managers, who face large maintenance cost in order to secure proper turnouts operability. Railway turnouts are complex mechanical systems, whose dynamic performance depends on the health state of the different components of the superstructure and substructure. A key component is the ballast as it provides the elastic support to the track and the sleepers and it largely contributes to the safety and reliability of the infrastructure. Ballast degradation can be a root cause of excessive failures in other components. A track recording car is typically used to collect geometry data that is used to assess the quality of the railway tracks; however this type of data has not been widely used for ballast quality evaluation in turnouts. One reason is that maintenance decision for turnouts are dominantly made based on visual inspections and/or manual measurement of track geometry, as turnouts are significantly more complex that traditional railway track. This study presents the application of fractal dimensioning of track longitudinal level for the monitoring of ballast degradation in railway turnouts. In other words, the irregularities of the track vertical profile related to the ballast degradation are quantified as a ballast quality index. The ballast quality index is the basis for developing ballast degradation models in different sections of the turnout based on a segmentation scheme. Using track geometry data of 88 turnouts in the Danish railway network for the period 2012-2017, this study develops and compares ballast degradation models based on regression analysis and stochastic processes (lognormal and Gamma processes). The models are estimated for different sections of the single turnout, for different turnouts at distinct geographical locations. The proposed method provides an efficient tool for the analysis of the effect of tamping on ballast degradation rate. Moreover, the effects on ballast degradation of track loading rate, train speeds and seasonal changes of weather conditions are quantified.
From Data to Physics
Authors: Michau Gabriel 
Published: March 2024

Data-driven Prognostic and Health Management is gaining more and more popularity, to the point where it is often seen as a potential solution for any maintenance problem. To the opposite, it is known that, when the physic of a system is known and tractable, it is usually the most efficient way to infer relevant indicators for detecting system behaviour changes.
And sometimes, when the physic of a system is only partially known, data-driven approaches can highlight critical information that would help to make the right physical assumptions, which in turn, can be used for elaborating the right health indicators.

This paper presents a real-life example of such process. With focus on the estimation of the degradation of a measurement system head, a data-driven approach, with its limitations, helped to identify the right signal processing tools with which to analyse the data.
With the results obtained from Spectral Analysis with Windowed Fourier Transform, the physics of the system could be hypothesized, and then used for deriving the most relevant indicators for monitoring the degradation. These indicators have been implemented online and so far used successfully on several machines encountering degradation of their measurement head.

This successful ``reversed'' case study of degradation monitoring is a strong reminder of the benefits of field symbiosis: data-driven approach made possible the understanding of the physics, which in turn could be used for data monitoring.
Fleet PHM for Critical Systems
Authors: Michau Gabriel , Palmé Thomas , Fink Olga 
Published: March 2024

Data-driven approaches are highly relying on the representativeness of the dataset used for training the algorithms. For Prognostics and Health Management (PHM) applications, a lack of representativeness will result in detecting new operating conditions (that have not yet been observed during the period used for training) as faults. This is particularly a challenge for PHM in critical systems, for which long and consistent datasets with all operating conditions are generally not available.

Among the many data-driven approaches applied to PHM, Deep Learning has recently brought promising results, enabling an automation of traditional PHM tasks, including signal processing, feature engineering and signal monitoring. Yet, as the parameters to optimize in a Deep Neural Networks are numerous, the training requires also huge datasets: another quality that critical system datasets are often missing.

When a fleet of systems is monitored, however, a solution to compose more representative and bigger training datasets is to combine condition monitoring data from systems with similar operating conditions. Identifying similar operating conditions would, traditionally, require comparing the distances and the distributions of multi-dimensional time series, a computationally intensive task worsened by the curse of dimensionality.

In this paper, we propose to use a deep neural network, designed for measuring similarities between the training and the testing datasets: a Hierarchical Extreme Learning Machine (HELM). HELM have demonstrated excellent abilities to jointly learn features and monitor deviations from the training data. Training first this network on individual systems, HELM can be used to identify other systems with similar operating conditions. Afterwards, the same network can be trained again with this representative dataset composed of condition monitoring data of several systems, to monitor more efficiently the health of the individual systems. Any deviation in the algorithm output would signify that the system is not anymore in operating conditions seen in the training fleet, and is likely experiencing a fault.

The novelty of the proposed approach lies in the usage of the same architecture twice in a bi-level framework: first, for selecting the representative datasets from a fleet of systems and second, using the selected datasets to train the health monitoring algorithm. This approach achieves good performances on both tasks. Learning from the fleet attenuates the impact of changing operating conditions (\eg summer/winter trends), and improves the quality of the fault detection.

The approach is tested on a fleet of 112 power plants, some of which experienced a stator vane failure.
Automated Failure Diagnosis in Aviation Maintenance Using Explainable Artificial Intelligence (XAI)
Authors: Zeldam Sophie ten , Jong Arjan de , Loendersloot Richard , Tinga Tiedo 
Published: March 2024

An incorrect or incomplete repair card, typically used in aviation maintenance for reporting failures, may result in incorrect maintenance and make it very hard to analyse the maintenance data. There are several reasons for this incomplete reporting. Firstly, (part of) the information is often unknown at the moment the maintenance crew fills in the card. Also, the findings on repair cards are generally filled out as freeform text, making it difficult to automatically interpret the findings. An automatically assessed failure description will lead to more complete and consistent repair cards. This willalso improve the efficiency of troubleshooting since this failure diagnosis can add information which would otherwise not be at the disposal of the maintenance crew at that time. This research will utilise a data driven approach combining maintenance and usage data. The model will be based on Artificial Intelligence (AI) such that it is no longer necessary to completely understand the physics of a (sub)system or component. XAI (eXplainable AI) will be added to the model to provide transparency and interpretability of the assessed diagnosis. The different steps towards this failure diagnosingmodel are applied to a case study with a main wheel of the RNLAF (Royal Netherlands Air Force) F-16. This preliminary feasibility study already showed the value of this automated failure diagnosis model with an improvement in diagnosis accuracy from 60% to 69%
Towards Self-aware and Environment-aware Power Converters
Authors: Bueno Mariani Guilherme , Foube Laurent , Chapalain Nadine , Kawahara Chihiro , Rojko Andreja , Degrenne Nicolas 
Published: March 2024

This paper develops the vision of power converter as a sensor for D&P (Diagnostic & Prognostic) of electrical energy systems. Such power converter would rely on available on-board intelligence & sensors. The converters are capable of monitoring components within the converters themselves, i.e. self-awareness and also the systems to which they are connected (e.g. motor, cables...), i.e. environmental awareness.
This vision is supported in this paper by an analysis of the literature. For a selection of components and systems prone to failure, key methodological steps are outlined. They include analysis of failure modes, of their cause and consequences. Then, stressors and precursors to failure are listed in a context of observability at the converter level. Means for processing this measurable data and extracting the useful information such as state of health, remaining lifetime and fault location are exemplified.
In-depth Feature Selection for PHM System's Feasibility Study for Helicopters' Main and Tail Rotor Actuators
Authors: De Martin Andrea , Jacazio Giovanni , Nesci Andrea , Sorli Massimo 
Published: March 2024

As deeply complex machines subjected to heavy vibratory environment, helicopters require relative low mean time between overhaul and suffer from high maintenance cost and availability issues. So far, PHM for helicopters has been aimed at detecting the presence of structural defects in the most critical parts of the mechanical transmission conveying power from the engine to the rotor blades, but very little has been presented on other flight-critical components, such as the main and tail rotor actuators. The proposed paper is focused on preliminary diagnostics and prognostics considerations for a traditional configuration of hydraulic solution, where a tandem actuator is aided by a Stability and Command Augmentation System (SCAS) during operations.
At first, the case-study is introduced and the simulation model employed for the analysis is described. Hence, two different failure modes affecting the SCAS are investigated and the physical models used to describe their progression are presented. In-depth data mining is then applied to achieve an accurate feature selection from raw data and an original way to visualize features' performances through an accuracy-sensitivity plane is proposed. Lastly, a particle filtering approach is adopted for failure prognosis and its output evaluated through traditional PHM metrics to assess the algorithm effectiveness. The present research provides encouraging results regarding the opportunity of realising a PHM system for helicopters' flight control actuators without the need of additional sensors, which could make solutions based upon the presented work feasible for both in-service and future platforms.
Opportunities for Explainable Artificial Intelligence in Aerospace Predictive Maintenance
Authors: Shukla Bibhudhendu , Fan Ip-Shing , Jennions Ian 
Published: March 2024

This paper aims to look at the value and the necessity of XAI (Explainable Artificial Intelligence) when using DNNs (Deep Neural Networks) in PM (Predictive Maintenance). The context will be the field of Aerospace IVHM (Integrated Vehicle Health Management) when using DNNs. An XAI (Explainable Artificial Intelligence) system is necessary so that the result of an AI (Artificial Intelligence) solution is clearly explained and understood by a human expert. This would allow the IVHM system to use XAI based PM to improve effectiveness of predictive model. An IVHM system would be able to utilize the information to assess the health of the subsystems, and their effect on the aircraft. Even if the underlying mathematical principles are understood, they lack an understandable insight, hence have difficulty in generating the underlying explanatory structures (i.e. black box). This calls for a process, or system, that enables decisions to be explainable, transparent, and understandable. It is argued that research in XAI would generally help to accelerate the implementation of AI/ML (Machine Learning) in the aerospace domain, and specifically help to facilitate compliance, transparency, and trust. This paper explains the following areas:

Challenges & benefits of AI based PM in aerospace
Why XAI is required for DNNs in aerospace PM?
Evolution of XAI models and industry adoption
Framework for XAI using XPA (Explainability Parameters)
Discussion about future research in adopting XAI & DNNs in improving IVHM.