Miracle-dz/UniFAR-FTUs huggingface dataset:
Age Invariant Face Recognition Via Aging Modelling

Aging is a complex problem because at different age points different changes occur in the human face. From childhood to teenage the changes are mostly related to craniofacial growth. At maturity the changes are mostly related to the skin color changes and the face skin starts becoming slack and less smooth. So aging is a mixture of all these components. Moreover, aging is a slow, irreversible and a process that is unique to every human being. Many factors affect the aging process. For example every person has different genes, blood group, and life style and belongs to a particular ethnic group. In order to resolve all these issues we need modelling method that should capture all changes throughout the aging process. So we deal shape and texture separately and finally combine them to develop a synergy between the two. The main objective of thesis is to develop novel aging growth models by using spatiotemporal modeling. It aims to develop and enhance our knowledge of craniofacial growth of human faces and capturing it to form models to understand the changes occurring due to aging process. We investigate five different types of models built on different levels of data granularity and with different perspective. At the global level a model is built on training data that encompasses the entire set of available individuals, whereas at the local level data from homogeneous sub-populations is used and finally at the individual level a personalized model is built for each individual. We used anthropometric features for extracting the shape information of face and for texture information we extract features based on the edges on the texture of the human face. Finally, we make integrated model in order to utilize synergy between three models and arrive at an optimum solution. Our Integrated model is inspired by the Integrated
Macrophyte influence on the structure and productivity of photosynthetic picoplankton in wetlands

We used multiple approaches to analyze photosynthetic picoplankton (PPP) structure and production in a wetland in the Lower Parana Basin (Argentina). A seasonal field survey was combined with an experimental study to analyze PPP variability under different light conditions. Flow cytometry analyses showed differences in PPP structure among the aquatic environments. Three main picoplankton populations were distinguished: phycocyanin-rich picocyanobacteria (Pcy), picoeukaryote (Peuk) and anaerobic anoxygenic photosynthetic bacteria (AnAnPB). The experimental study revealed important changes in PPP structure in relation to the light conditions imposed by floating plants: enclosures exposed to light showed a higher proportion of Peuk and Pcy over AnAnPB; in mesocosms covered by plants, just as in the highly vegetated relict lakes (ROLs), AnAnPB were dominant. Total picophytoplankton abundances varied from 1.7 × 10 4 to 4.6 × 10 5 cells mL -1 in the shallow lakes, and were lower (0.69 × 10 4 to 2.5 × 10 5 cells mL -1 ) in the ROLs. Annual variations in temperature and hydrological conditions influenced the PPP abundances, observing maximum values during the warm dry phase. The photosynthetic rates per unit area of PPP (P APPP ) and algae >3 μm (P A>3 μm ) were measured in the aquatic environments in winter and summer: P A PPP (1.5 to 100 mg C m -2 h -1 ) was lower than P A >3 μm and was directly affected by light, which was limiting under the dense floating plant cover.
EXPERIMENTAL STUDY OF UNLIPPED CHANNEL BEAMS SUBJECT TO WEB CRIPPLING UNDER ONE FLANGE LOAD CASES

Cold-formed steel members are becoming increasingly popular in the building industry due to their superior strength to weight ratio and ease of fabrication as opposed to hot-rolled steel members. However, they are susceptible to various buckling modes at stresses below the yield stress of the member because of their relatively high width-to-thickness ratio. Web crippling is one of the failure modes that occurs in steel channel sections under transverse concentrated loads or reactions. Recently a test method has been proposed by AISI to obtain the web crippling capacities under both one-flange and two-flange load cases. Using this test method 21 tests were conducted in this research to investigate the web crippling behaviour and strengths of an unlipped channel section with stocky webs known as DuraGal Channels under end-one-flange (EOF) and interior one-flange (IOF) load cases. DuraGal channels with different web slenderness and bearing lengths were tested with their flanges unfastened to supports. In this research the suitability of the currently available design rules for unlipped channels subject to web crippling under one flange load cases was investigated, and suitable modifications were proposed where necessary. This paper presents the details of this experimental study and the results.
Monitoring the Stability of a Growing Organic Corpus, with special reference to Sepedi and Xitsonga

i; "? this article it is assumed that the modern lexicographer . who is drafting the plan of especially a new dictionary for a particular language will have electronic corpora at his/her disposal. An electronic corpus can be defined in an oversimplified way as a com- puterised collection of texts. The total number of running words, or "tokens," in all those texts together, corresponds to the size of the cor- pus, while the total number of different words are referred to as "types. " For instance, a small corpus consisting of 10 chapters of a Ghent University annual report has 5,502 tokens, but only 1,246 types. The lexicographer then utilises sophisticated computer interface pack- ages, called "corpus query tools," to analyse such a corpus in various ways. Firstly, the total number of occurrences of a particular word, the "raw frequency," and the spread of that word across texts are studied. For example, from Table 1, which shows the top 10 types in the annual- report corpus, one sees that the word university has a total frequency of 68 (which corresponds to 1.24% of all the tokens in the corpus). The spread or distribution of university across the different texts is shown in Table 2. From Table 2 one sees that the spread ofwords can be studied across texts (16 times in chapter 8, 6 times in chapter 2, etc.) and text-internally (each stroke in the dispersion plots corresponds to an occurrence of university) . Raw, across-text and text-internal frequen- cies assist in making decisions either for inclusion ofdata in or for omis- sion from the dictionary. Although frequencies adjusted by text distrib- ution enable a more detailed analysis, we will use raw frequencies
A field‐tested robotic harvesting system for iceberg lettuce

Agriculture provides an unique opportunity for the development of robotic systems; robots must be developed which can operate in harsh conditions and in highly uncertain and unknown environments. One particular challenge is performing manipulation for autonomous robotic harvesting. This paper describes recent and current work to automate the harvesting of iceberg lettuce. Unlike many other produce, iceberg is challenging to harvest as the crop is easily damaged by handling and is very hard to detect visually. A platform called Vegebot has been developed to enable the iterative development and field testing of the solution, which comprises of a vision system, custom end effector and software. To address the harvesting challenges posed by iceberg lettuce a bespoke vision and learning system has been developed which uses two integrated convolutional neural networks to achieve classification and localization. A custom end effector has been developed to allow damage free harvesting. To allow this end effector to achieve repeatable and consistent harvesting, a control method using force feedback allows detection of the ground. The system has been tested in the field, with experimental evidence gained which demonstrates the success of the vision system to localize and classify the lettuce, and the full integrated system to harvest lettuce. This study demonstrates how existing state‐of‐the art vision approaches can be applied to agricultural robotics, and mechanical systems can be developed which leverage the environmental constraints imposed in such environments.
Acceptance of altmetrics by LIS scholars: An exploratory study

A survey of iSchool and Library and Information Science American Library Association accredited school heads explored their use and opinions about academic social media platforms. Results show that ResearchGate is the venue of choice, although respondents do not believe maintaining a profile on academic social media platforms influences academic careers. Respondents do see the value of academic social media sites to enhance visibility and reputation. These data are complemented with an analysis of the visibility on academic/professional social media platforms of this group. The altmetric indicators derived from the data were compared to traditional bibliometric indicators.
Resilience Informed Integrity Management of Wind Turbine Parks

A novel framework for resilience modeling of wind turbine parks is proposed in support of optimization of decisions on asset integrity management. The concept of resilience originating from natural and social sciences is adapted here to facilitate the joint optimization of decision alternatives related to design, with decision alternatives addressing organizational performance. The generic probabilistic systems representation framework by the Joint Committee on Structural Safety (JCSS) (2008) is utilized to establish a scenario-based modeling of how different types of disturbances may lead to damages and failures of systems and sub-systems of wind turbine parks, together with associated direct and indirect consequences. Special emphasis is directed on the consistent probabilistic representation of the uncertainties and the stochastic and causal dependencies within the wind turbine park system. The framework facilitates the identification of optimal asset integrity management decision alternatives that fulfill given requirements to resilience. The potentials associated with the use of the framework are highlighted by an example considering a wind turbine park with ten identical wind turbines, with each modelled as a system of mechanical, electrical, and structural sub-systems. The resilience performance characteristics of the wind turbine park, such as the expected value of generated service life benefits, the expected value of production down time, and the probability of resilience failure are modelled and quantified such as to support the ranking of decision alternatives relating to the design of the wind turbine sub-systems, the level of organizational preparedness, the percentage of the generated service life benefits to be kept to ensure sufficient economic capacity to deal with future disturbances, and the stock-keeping of essential spare parts.
Measurements and analysis of adhesive forces for micron particles on common indoor surfaces

Dust particle resuspension from indoor surfaces is an important source of particulate matter in indoor environments. The adhesive force represents the resistance of the particle to resuspension in an air stream and is a key factor in the resuspension process. The present study used an atomic force microscope (AFM) to measure the adhesive forces between three particle samples and four common indoor surfaces including acrylic, marble, epoxy flooring and wood flooring. The effects of different indoor surfaces were investigated using 12 particle–surface combinations. The results show that the average adhesive forces range from 8.2 nN to 448.1 nN for different combinations with the surface roughness being the main factor. The average adhesive force increases with the contact radius and is larger on the wood flooring surface than on the other three surfaces. Then, the resuspension process was simulated using the moment balance method with the measured adhesive forces as the model inputs. The model predictions show that the wood flooring surface has the largest resistance to the air flow entrainment, followed by the epoxy flooring surface, then the marble surface and the least is the acrylic surface.
DCNN-Based Multi-Signal Induction Motor Fault Diagnosis

Deep learning (DL) architecture, which exploits multiple hidden layers to learn hierarchical representations automatically from massive input data, presents a promising tool for characterizing fault conditions. This paper proposes a DL-based multi-signal fault diagnosis method that leverages the powerful feature learning ability of a convolutional neural network (CNN) in images. The proposed deep model is able to learn from multiple types of sensor signals simultaneously so that it can achieve robust performance and finally realize accurate induction motor fault recognition. First, the acquired sensor signals are converted to time–frequency distribution (TFD) by wavelet transform. Then, a deep CNN is applied to learning discriminative representations from the TFD images. Since then, a fully connected layer in deep architecture gives the prediction of induction motor condition based on learned features. In order to verify the effectiveness of the designed deep model, experiments are carried out on a machine fault simulator where both vibration and current signals are analyzed. Experimental results indicate that the proposed method outperforms traditional fault diagnosis methods, hence, demonstrating effectiveness in induction motor application. Compared with conventional methods that rely on delicate features extracted by experienced experts, the proposed deep model is able to automatically learn and select suitable features that contribute to accurate fault diagnosis. Compared with single-signal input, the multi-signal model has more accurate and stable performance and overcomes the overfitting problem to some degree.
Reference Voltage Vector Based Model Predictive Torque Control with RMS Solution for PMSM

To reduce the computational burden of a conventional model predictive torque controller (MPTC), a reference voltage vector based MPTC strategy is proposed. The reference voltage vector is obtained from the reference stator flux vector and the reference torque. According to the location of the reference voltage vector, a first optimal vector can be determined in a quite straightforward way, improving the system dynamic performance. Furthermore, in order to decrease the torque and flux ripple, a root mean square (RMS) based solution is employed to generate the reference voltage vector and calculate the duty ratio. This method aims at minimizing the RMS error of flux and torque during the whole control period. Then, the steady state performance is improved. Besides, since the new cost function contains only the reference voltage vector, the weighting factor in conventional MPTC is eliminated. In addition, to keep a balance between the steady state performance and switching frequency, the candidates for the second optimal vector are restricted to a certain scope. Simulations were carried out and the results verified the validation of the proposed MPTC strategy.
A Wi-Fi Cloud-Based Portable Potentiostat for Electrochemical Biosensors

The measurement of the analyte concentration in electrochemical biosensors traditionally requires costly laboratory equipment to obtain accurate results. Innovative portable solutions have recently been proposed, but usually, they lean on personal computers (PCs) or smartphones for data elaboration and they exhibit poor resolution or portability and proprietary software. This paper presents a low-cost portable system, assembling an ad hoc-designed analog front end (AFE) and a development board equipped with a system on chip integrating a microcontroller and a Wi-Fi network processor. The wireless module enables the transmission of measurements directly to a cloud service for sharing device outcome with users (physicians, caregivers, and so on). In doing so, the system does not require neither the customized software nor other devices involved in data acquisition. Furthermore, when any Internet connection is lost, the data are stored on board for subsequent transmission when a Wi-Fi connection is available. The noise output voltage spectrum has been characterized. Since the designed device is intended to be battery-powered to enhance portability, investigations about battery lifetime were carried out. Finally, data acquired with a conventional benchtop Autolab PGSTAT-204 electrochemical workstation are compared with the outcome of our developed device to validate the effectiveness of our proposal. To this end, we selected ferri/ferrocyanide as redox probe, obtaining the calibration curves for both the platforms. The final outcomes are shown to be feasible, accurate, and repeatable.
An Online Global Fault-Tolerant Control Strategy for Symmetrical Multiphase Machines With Minimum Losses in Full Torque Production Range

The high fault-tolerant ability of multiphase drives is favored in safety-critical applications. Under open-phase faults, to guarantee ripple-free torque, stator current references should be revised. In this paper, a global fault-tolerant control strategy based on an online current optimization algorithm (OCOA) is proposed for symmetrical multiphase machines (SMMs) to achieve both the maximal torque production range (TPR) and minimal stator winding losses under all possible OPFs. The OCOA can calculate the minimum losses current references covering the full TPR online, regardless of the number and locations of faulty phases. With the online-optimized references, ripple-free torque and minimum losses in full TPR can be achieved under faulty conditions. Additionally, the proposed method can be adopted to SMMs with arbitrary phase numbers. Simulation and experiments demonstrate that the online optimization can be completed in a short time, meanwhile, achieves the same TPR and loss reduction compared with an existing offline strategy based on prestored look-up tables. Due to the flexible online calculation ability and excellent extensibility, the proposed method is especially favored in modular multiphase converters and machines with high phase numbers, where converters can adapt to machines with different phase numbers or the number of possible faulty conditions can be extremely large.
Malicious Corruption Resilience in PMU Data and Wide-Area Damping Control

This paper presents a framework for malicious corruption resilience in phasor measurement unit (PMU) data and a methodology for applying this in wide-area damping control. The problem of detecting corruptions in raw PMU measurements is formulated as a compressed sensing problem and the compromised signals are recovered using an <inline-formula> <tex-math notation="LaTeX">$l_{p}$ </tex-math></inline-formula>-norm (<inline-formula> <tex-math notation="LaTeX">$0 < p < 1$ </tex-math></inline-formula>)-based online robust principal component analysis (RPCA) algorithm. The performance of the proposed method has been compared for different patterns of corruption with an <inline-formula> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula>-norm-based RPCA algorithm. The effectiveness of using the proposed data preprocessing architecture for correcting raw PMU feedback signals corrupted by missing data attack was demonstrated for closed-loop wide-area power oscillation damping control in 16-machine, 5-area New England-New York system.
Taxi-Based Mobility Demand Formulation and Prediction Using Conditional Generative Adversarial Network-Driven Learning Approaches

In this paper, a deep learning (DL) framework was proposed to predict the taxi-passenger demand while the spatial, the temporal, and external dependencies were considered simultaneously. The proposed DL framework combined a modified density-based spatial clustering algorithm with noise (DBSCAN) and a conditional generative adversarial network (CGAN) model. More specifically, the modified DBSCAN model was applied to produce a number of sub-networks considering the spatial correlation of taxi pick-up events in the road network. And the CGAN model, fed with the historical taxi passenger demand and other conditional information, was capable to predict the taxi-passenger demands. The proposed CGAN model was made up with two long short-term memory (LSTM) neural networks, which are termed as the generative network ${G}$ and the discriminative network ${D}$ , respectively. Adversarial training process was conducted to the two LSTMs. In the numerical experiment, different model layouts were compared. It was found that different network layouts provided reasonable accuracy. With limited training data, more LSTM layers in the generator network resulted in not only higher accuracy, but also more difficulties in training. Comparisons were also conducted between the proposed prediction model and four typical approaches, including the moving average method, the autoregressive integrated moving method, the neural network model, and the LSTM neural network model. The comparison results showed that the proposed model outperformed all the other methods. And the repeated experiment indicated that the proposed CGAN model provided significant better predictions than the LSTM model did. Future research was recommended to include more datasets for testing the model and more information for improving predictive performance.
Bio-Kinematic Design of Individualized Lift-Assist Chairs for the Support of Sit-to-Stand Movement

Rising from a chair is a fundamental movement in daily life and a prerequisite for independent functional ability. Yet, it remains one of the most biomechanically demanding activities as it requires high levels of neuromuscular coordination, muscle strength and postural control. While standing up from a seated position is often implied to be a ubiquitous skill it becomes increasingly difficult with age. To prolong the independence of elderly a novel computational design procedure for lift-assist mechanisms is presented. This procedure individualizes the dimensions of a mechanism to the natural sit-to-stand movement of the patient while complying with the limited space given by the applicable furniture. Given marker-based sit-to-stand motion data task positions are defined to carry out a finite position synthesis of a four-bar linkage that provides patient-specific guidance of a seat. The four-bar linkage combined with the lower limb of a patient generates a biologically inspired six-bar linkage. Thus, accomplishing a bio-kinematic design of linkages where this thesis provides an exemplary design session.
Virtual field trip for mobile construction safety education using 360-degree panoramic virtual reality

Constructionsafety education plays a vital rolein equipping studentswithconcrete safety knowledge and promoting safetyperformance prior to entering construction sites. However, safety topics are not given adequate attention in mostconstruction curricula and traditional pedagogic tools fail to provide practical experience or sufficiently engage students inacquiring safety knowledge. With this regard, this study proposes the VIrtual FIeld Trip System (VIFITS) for mobileconstruction safety education using a 360-degree panoramic virtual reality. The VIFITS utilizes a state-of-the-art 360-degree panorama-based Virtual Reality technology in order to bring construction field trips to the classroom and providepractical experience to improve students’ safety knowledge. VIFITS consists of three modules: (1) a Safety InformationDissemination module (SID) in which the educator disseminates safety information to the students through mobilelearning; (2) a Virtual Field Trip Experience module (FTE) where students experience virtual construction field trips basedon their own mobile devices; (3) and a Safety Knowledge Assessment module (SKA) designed for learners to play testingsimulation games in order to assess their safety knowledge. The VIFITS prototype is developed and validated with virtualscenarios derived from real construction sites. The results reveal that the VIFITS is a powerful pedagogical method foreffectively providing practical experience and safety knowledge for students.