Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning for HRRP-based Satellite Recognition

An approach that employs deep learning technology is presented to recognize satellites based on radar high-resolution range profile (HRRP) data. We focus on extracting effective satellite recognition features in this paper. Thus, a deep learning model is constructed by gated recurrent unit (GRU) neural network and support vector machine (SVM) to extract more abstract and accurate features. Firstly, the radar HRRP data of four satellites is obtained by simulation. And data preprocessing has been done according to HRRP characteristic. Next, a GRU-SVM model is set up and some deep learning skills, such as dropout and cross validation, have been applied to improve recognition accuracy. The training results of GRU neural network show their effectiveness. In order to demonstrate the superiority of this approach, five other feature extraction methods have been used as a comparison based on clean satellite HRRP data and noisy data. The experiment results show that the presented GRU-SVM model could recognize satellites effectively and accurately, and has better recognition performance and noise robustness compared with five other methods.
Thermal Performance of the Graphene Oxide Nanofluids Flow in an Upright Channel Through a Permeable Medium

The three-dimensional flow of Water based Graphene Oxide (GO-W) and Ethylene Glycol based Graphene Oxide (GO-EG) nanofluids amongst the binary upright and parallel plates is considered. The unsteady movement of the nanofluid is associated with the porous medium and the uniform magnetic field is executed in the perpendicular direction of the flow field. The basic governing equations have been altered using the Von Karman transformation, including the natural convection in the downward direction. The solution for the modeled problem has been attained by means of optimal homotopy analysis method (OHAM). The influence of the physical parameters on the momentum boundary layer, pressure, and temperature fields is mainly focused. Moreover, the comparison of the GO-W and GO-EG nanofluids under the impact of physical constraints has been analyzed graphically and numerically. The imperative physical constraints of the drag force and heat transfer rate have been computed and discussed. The results have been validated using the error analysis and the obtained outcomes have been shown and discussed. The obtained results are compared with the numerical ND-Solve method. It is obtained that the increasing order of the iterations reduces the absolute error and the results go to the strong convergence. Moreover, the impact of the physical parameters is more precise in the GO-EG nanofluid due to its higher thermophysical properties.
Analytical Prediction of the Radiation Characteristics of $2\times2$ Ridge Gap Waveguide Slot Antenna Array With Suppressed Grating Lobes at the V-band

In this paper, the radiation patterns due to magnetic sources embedded in a ground plane in the presence of a partially reflective surface are analytically formulated and calculated. Love’s equivalence principle, the transmission-line analogy, and the reciprocity theory are all combined and employed to calculate the far-fields of a four-element $(2\times 2)$ slot array excited by the printed ridge gap waveguide technology operating in the V-band. Since the spacing between the array elements is greater than one wavelength, grating lobes cannot be avoided. Hence, to suppress the grating lobes and enhance the antenna directivity, a dielectric superstrate with the proper properties is utilized. The slot antenna array is operating at 60 GHz for millimeter-wave applications. The obtained results from the proposed analytical formulation agree very well with full-wave numerical simulations obtained from the EM commercial tool HFSS. It is worthwhile to mention that the presented analytical solution is about 30 times faster than its full-wave simulator counterpart.
A Multicharger Cooperative Energy Provision Algorithm Based on Density Clustering in the Industrial Internet of Things

Wireless sensor networks (WSNs) are an important core of the Industrial Internet of Things (IIoT). Wireless rechargeable sensor networks (WRSNs) are sensor networks that are charged by mobile chargers (MCs), and can achieve self-sufficiency. Therefore, the development of WRSNs has begun to attract widespread attention in recent years. Most of the existing energy replenishment algorithms for MCs use one or more MCs to serve the whole network in WRSNs. However, a single MC is not suitable for large-scale network environments, and multiple MCs make the network cost too high. Thus, this paper proposes a collaborative charging algorithm based on network density clustering (CCA-NDC) in WRSNs. This algorithm uses the mean-shift algorithm based on density to cluster, and then the mother wireless charger vehicle (MWCV) carries multiple sub wireless charger vehicles (SWCVs) to charge the nodes in each cluster by using a gradient descent optimization algorithm. The experimental results confirm that the proposed algorithm can effectively replenish the energy of the network and make the network more stable.
Application of Improved Multi-Objective Bacterial Foraging Algorithm in Virtual Network Mapping Algorithm

For the virtual network mapping problem, consideration of many aspects will be more comprehensive. In this paper, the multi-objective bacterial foraging optimization algorithm is improved to solve this problem. First, because the virtual network mapping problem is a discrete problem, the operator of the algorithm is redefined as discrete; secondly, the resource congestion factor and 2-opt algorithm are introduced in the chemotaxis operation of the algorithm, and the cross-factor is introduced in the copy operation, so that the found solution is better. At the same time, the node resource load balance degree and the minimum cost are used as the fitness function, so that the found solution solves the problem more. The experimental results show that the algorithm is used to solve the virtual network mapping problem. The algorithm has good validity and stability in both large-scale virtual network requests and standard cases.
Attention-Based Character-Word Hybrid Neural Networks With Semantic and Structural Information for Identifying of Urgent Posts in MOOC Discussion Forums

The MOOC Discussion Forum is the place where students and teachers communicate, often plagued by information overload and confusion. Posts that students used to express confusion and demanded teachers’ attention are most likely to be overwhelmed by the amount of noise in the forum. Therefore, how to pay attention to urgent posts in time has become a critical problem to be solved. In this paper, we present a new hybrid neural network for identifying “urgent” posts that require immediate attention from instructors. We proposed a semantic and structure extraction part including convolutional neural network (CNN) and gated recurrent unit (GRU), which can simultaneously learn the semantic information and structural information of sentences. In addition, Due to a lot of noise such as spelling mistakes and emoticons in the forum comment text, we propose to use Character-level Convolutional Networks (Char-CNN) to capture these special information. Finally, the semantic and structural information learned by the semantic and structural extraction part is merged with the character information learned by Char-CNN, and the attention mechanism to learn their weights, the final representation of the sentence can be obtained. In our experiments, we achieve urgent posts classification with a micro F-score of 91.8%, 91.3% and 88.4% on the Stanford MOOCPosts dataset, outperforming the state-of-the-art approach by 1.8%, 2.4% and 1.5% respectively. This work can help teachers prioritize their responses and better manage numerous posts. Teachers can answer learner questions in a timely manner and help reduce dropout rates and improve completion rates.
Verbs as nouns: empirical investigations on event-denoting nominalizations

In this thesis, I study the differences in form and interpretation presented by event-denoting nominalizations. Frequently, languages have more than one type of event nominalization, such as deverbal nouns derived by means of suffixes (Italian mutamento / mutazione, ‘change’, ‘mutation’, or English assignment, explosion) and their corresponding verbal nouns, e.g. infinitives (il mutare, ‘the changing’) or gerunds (exploding). These are usually perceived as alternatives, since their semantic difference is not clearly understood by neither native speakers nor linguists. The aim of this work is to understand the rationale that leads us to choose one form instead of the other and to define the linguistic features involved. The hypothesis underlying the whole thesis is that different forms are never true synonyms and, thus, present some differences in use, distribution or meaning. In a first study, I explore the role of the base verb in the nominalization selection. I investigate if the various nominalizations are formed from different types of base verbs and which characteristics define their domain of application. By means of statistical modeling, I highlight how the transitivity of the base verb partially determines which nominalization is preferred. Moreover, I show that NIs are not used to make up for the lack of a corresponding EDN, refuting previous claims. Then, I move forward analyzing the cases in which both forms are derived from the same base and I try to understand if they differ in meaning. In the second study presented, I use collocation analysis to observe their semantic dissimilarities. With focus on a single syntactic pattern, I find out that nominal infinitives and deverbal nouns inherit only part of the base verb senses. The former usually prefer metaphorical and abstract senses, whereas the latter select more concrete and literal ones. Lastly, I use distributional semantic models to observe quantitatively the semantic shift of the two processes. I confirm the hypothesis that nominal infinitives are more transparent and more semantically regular than deverbal nouns, given their inflectional nature. The studies presented have been conducted on Italian and German; however, the findings are relevant for the general treatment of nominalizations and may be replicated for further languages. Overall, my work shows how quantitative analyses of corpus data can help us investigate problems that are hardly addressed by linguists introspection. Moreover, it includes in the study of nominalizations nominal infinitives, non-finite verbal forms which, contrary to English gerunds, have not received the attention they deserve.
A Query System for Extracting Requirements-Related Information from Legal Texts

Searching legal texts for relevant information is a complex and expensive activity. The search solutions offered by present-day legal portals are targeted primarily at legal professionals. These solutions are not adequate for requirements analysts whose objective is to extract domain knowledge including stakeholders, rights and duties, and business processes that are relevant to legal requirements. Semantic Web technologies now enable smart search capabilities and can be exploited to help requirements analysts in elaborating legal requirements. In our previous work, we developed an automated framework for extracting semantic metadata from legal texts. In this paper, we investigate the use of our metadata extraction framework as an enabler for smart legal search with a focus on requirements engineering activities. We report on our industrial experience helping the Government of Luxembourg provide an advanced search facility over Luxembourg's Income Tax Law. The experience shows that semantic legal metadata can be successfully exploited for answering requirements engineering-related legal queries. Our results also suggest that our conceptualization of semantic legal metadata can be further improved with new information elements and relations.
Performance analysis of hybrid photovoltaic/wind energy system using KY boost converter

Nowadays tremendous increase in renewable energy technologies available in abounding nature energy extracted from sun, wind etc. This Proposed topology gathered maximum power using multi input ky boost converters for hybrid energy. This hybrid topology operates mainly delivered the power from renewable energy sources solar/wind to dc bus. In the absence of any one source, wind or solar supplies power to the dc bus. Without any renewable energy sources battery deliver the power to dc bus.An inverter type module convert the power from dc bus to ac grid. In this paper effectiveness of hybrid renewable energy is simulated through MATLAB/SIMULINK
Research on Face Image Encryption Based on Deep Learning

With the development of artificial intelligence and big data technology, the requirements for information security are increasing, and the role of biometrics in network security and information security authentication has also increased. Face recognition technology has been widely applied in many Internet payment platforms. This paper proposes a face recognition algorithm based on improved deep network automatic extraction feature, which can extract the discriminative features of the target more accurately and encrypt the face image to ensure the privacy and security of face recognition. In this paper, an automatic deep feature extractor is generated by preprocessing and fine-tuning, and then the hyperchaotic image is encrypted. Several common face databases are used to test in this algorithm and this results show that the algorithm has more availability than the traditional and general deep learning methods in terms of performance.
Intelligent Highway Traffic Forecast Based on Deep Learning and Restructured Road Models

We propose a highway traffic forecasting system that informs the traffic condition of highways from a few minutes to several months ahead. It can reflect the weather information of the regions of roads in the traffic data computation. We develop various road models to represent separate points of the highways based on traffic characteristics such as interchange, exit, endpoint, etc. Experimental results show our system outperforms a generic convolutional network model with 97.6% accuracy of travel-time prediction and the reduction by 30% of computing time for a moderate sized highway network.
Deriving patterns from animal movement decisions : a comparison of approximation techniques and a continuous-time resource selection method

This thesis consists of two parts. First, I investigate the effect of using three partial differential equation (PDE) techniques on analysing some simple animal movement models. Results of examining a biased random walk show that an old approach from Patlak’s work in 1953 can give a very poor approximation even in this very simple case, while more recent methods correctly describe the movement process. By analysing central-place foraging models and movement in heterogeneous landscapes, I show that more recent PDE techniques can provide more accurate approximations of space use patterns when the kernel describing the movement is sufficiently smooth. However, for non-smooth movement kernels, all methods can result in quantitatively misleading approximations. This analysis provides an insight into the conditions under which the PDE methods might perform better. Second, I present two continuous-time modelling frameworks for analysing animal movement depending on selection of resources over the whole landscape or in the surrounding area. The models are parameterised by a Markov chain Monte Carlo (MCMC) algorithm, allowing for movement decisions made at any time. Based on these frameworks, I generate simulations in various situations, including migration and foraging in patchy or rasterised landscapes. Analysis of simulated trajectories reveals that the inference algorithm can successfully capture the parameter values used in simulations in most cases. I also fit the migration model to spring migration data of some mule deer (Odocoileus hemionus). The results imply that migration might be explained by the trade-off between resources and travel distance. This work addresses some limitations of methods relying on discrete-time movement models and therefore provides an advanced tool for understanding movement driven by environmental factors.
Optimum Parameters for Fault Detection in Bioreactor Using Support Vector Machine and Neural Network

: Bioprocesses are parameter sensitive, highly non-linear and complex in nature. Apart from close monitoring of the process variables and good control strategy, a robust fault detection and diagnosis (FDD) strategy will help in maintaining the quality of the product by early detection of fault. This paper discusses the application of support vector machines (SVM) and artificial neural networks (ANN) in classifying the normal and faulty conditions in semi-batch bioreactor for penicillin production. The SVM classifier was implemented with three kernel functions viz. linear, polynomial and Gaussian radial basis function (RBF). In case of ANN, a back propagation two layer feed forward neural network was used. The optimum SVM and ANN models were found when the model produced the minimal error with respect to the training, testing and data validation. In case of SVM, it was found that linear kernel is not suitable for bioreactor data, whereas RBF outperformed the polynomial function. Further the performance of SVM was compared with ANN, and ANN outperformed SVM.
Guidance and Control of a Spacecraft Swarm with Limited Knowledge

There is no doubt that the smallest spacecraft platforms (CubeSats, NanoSats and microsats) are revolutionizing the market. The possibilities of developing spacecraft through standard platforms, commercial of the shelf components and then reduced the costs of development has allowed new organizations to enter the space section. On top of that, traditional businesses have been benefited by these type of new spacecraft, as more technology demonstrations and riskier missions have been able to be developed through this technology. Nevertheless, these spacecraft systems cannot compete with their larger counterparts due to their size limitations. The extra space will allow for larger spacecraft to accommodate more complex and precise instruments and payloads. Therefore, an individual CubeSat will never be able to compete with a larger system. Nevertheless, in the last two decades, the concept of using the combined power of these simple and cheap systems to perform a task only possible with larger spacecraft has been proposed. One of the most interesting possibilities among the use of these distributed space systems is the use of swarming. Swarming is a technique which consists of the combination of a large number of simple elements to allow for the emergence of properties that allow the whole system to act as more than just its parts. The benefits of spacecraft swarming are undeniable, as inherently swarming brings robustness, flexibility, and scalability to the system, all three properties extremely valued in space missions. \\ For the moment spacecraft swarming has been mostly based on generating formation patterns with a large degree of autonomy. Still, only a handful of techniques have been explored. This work aims to further advance the knowledge of spacecraft swarming techniques. To do so, the implementation of a swarming algorithm never tested in space will be studied The advantage of this algorithm is double. First, as all swarming techniques, it presents a certain degree of artificial intelligence that will allow the swarm to cope with unexpected situations. Second, by design, this algorithm requires really little knowledge of the swarm environment. This translates in less need for information processing between spacecraft and within each spacecraft. Since the application is focused on already spacecraft with limited capabilities, this is certainly advantageous. To fully understand the work here presented, it is recommended that the reader possesses graduate-level knowledge on orbital dynamics and control, as well as some undergraduate notions of computer programming and artificial intelligence techniques. If the reader lacks knowledge on any of the mentioned areas, it is recommended to read the associated literature study referenced in the bibliography.
Mining of Behavioral Motifs from Individual Ant Trajectory

Complex biological systems resemble a “black box,” as it is a priori unclear how interactions among individuals will aﬀect the collective (group) behavioral performance. Network analysis is a suitable method to shed light on these black boxes by studying the collective behavior of highly integrated social organisms such as ants. Individual within the colony have their own personality and task allocation for sustaining the society. Individual-level data are also important for understanding network structure. To obtain individual-level data such as personality and task allocation, individual behavior was assessed using several well-established information processing methods. To detect individual personalities from the position data, trajectory patterns were used. To analyze trajectory patterns, behavioral motif detection was used. The behavioral motif is the minimum unit of the entire trajectory.
Column concept determination based on multiple evidences

Tables on the web provide rich information. To make sufficient usage of web tables, the semantics of columns should be identified correctly. The absence, misspelling, and abbreviation in column names bring the challenges in column semantics identification. Facing this challenge, we extract multiple features including keywords, concepts, and structure from the content in the column. Thus, we could identify the column semantics by matching these multiple features. For the extraction and matching with these features, we propose efficient algorithms. Experimental results on real data sets show that our solution achieves high performance.
Towards Mixed-initiative generation of multi-channel sequential structure

We argue for the benefit of designing deep generative models through a mixed-initiative, co-creative combination of deep learning algorithms and human specifications, focusing on multi-channel music composition. Sequence models have shown convincing results in domains such as summarization and translation; however, longer-term structure remains a major challenge. Given lengthy inputs and outputs, deep generative systems still lack reliable representations of beginnings, middles, and ends, which are standard aspects of creating content in domains such as music composition. This paper aims to contribute a framework for mixed-initiative generation approaches that let humans both supply and control some of these aspects in deep generative models for music, and present a case study of Counterpoint by Convolutional Neural Network (CoCoNet).
A 2 to 4 GHz Instantaneous Frequency Measurement System Using Multiple Band-Pass Filters

In this paper, a 2 to 4GHz Instantaneous Frequency Measurement (IFM) system is presented. The proposed design uses four band-pass filters to estimate the frequency value of an RF signal. The proposed design is an improvement of that presented in our previously published work. In this work, a new frequency estimator is developed, and a new methodology for designing the frequency response of the band-pass filters is proposed. These two improvements show better accuracy in estimating the frequency value. A closed form for the Standard Deviation (STD) and the bias of the new estimator were derived, and used to adjust the frequency response of the filters. The fabricated system showed a maximum error of about 11 MHz, for practical values of noise and measurements errors.