Miracle-dz/UniFAR-FTUs huggingface dataset:
Satelit Non-GSO di Indonesia: Operasi Saat Ini dan Perkiraan Masa Depan [Indonesian Non-GSO Satellites: Current Operations and Future Predictions]

Indonesian satellite operations are mainly known for the operation of geostationary orbit (GSO) satellites for telecommunication missions. In the last decade, however, the activities of non-GSO satellites in Indonesia are significantly increasing. Therefore, the objectives of this research are to find out the cause of the growth and to predict the future operation of non-GSO satellites in Indonesia. For such purpose, review on the operation of non-GSO satellites in the past and now was done. Analysis on the characteristics of their missions, owners/operators, and technical characteristics of the satellites were done. Literature studies on the global trends and their defining strategic environments were also done to complete the insight. The study shows that increase in the use of non-GSO satellites is caused by the growth in remote sensing application, M2M application, and development of LAPAN’s satellites. In the future, the growth of non-GSO remote sensing satellite is predicted to be caused by the same reason. The increase in the use of non-GSO telecommunication satellites, however, will be affected more by the new global trend. The increase in non-GSO remote sensing satellites does not affect significantly on the needs of frequency and ground stations. The increase in the non-GSO telecommunication satellites, however, needs significant additional frequency allocations and ground stations.
Modeling and Control of Kite Energy Systems

Kite energy systems are an emerging renewable energy technology. Unlike conventional turbines, kite energy systems extract wind power using tethered kites which can move freely in the wind or underwater in an ocean current. Due to the mobility, kite power systems can harvest power from regions with higher and steadier power density by moving in high-speed cross flow motion. An airborne kite energy system harnesses wind power at an altitude higher than the conventional wind turbines, while an undersea kite energy system extracts power close to the ocean surface. In this dissertation, the physical limitation, mathematical modeling, and control system design of the kite energy systems are studied. First, three major physical effects that are acting on the kite energy systems are investigated, including potential force, steady aero-/hydro-dynamic force and added mass effects. Furthermore, the dissipativity of the steady aero-/hydro-dynamic forces with respect to the apparent velocity is established. Based on this analysis, the power generation limit of the kite energy systems is studied. A power limit formulation is given which generalize the two-dimensional result to three-dimensional case. The different physical phenomenon is modeled in different coordinate systems, the difference of the density, viscosity between air and water are significant, and the kite energy system can operate in two distinct modes. To combine different physical effects into a single simulation framework, the equivalences of the kite model in different coordinate systems are established through kinematic analysis. Using these equivalent relations, a unified simulation model for airborne and undersea kite energy systems are derived. The control system design of kite energy systems is also investigated. The resulting equations of motion of kite energy systems are highly nonlinear. Therefore, Lyapunov methods are used to
Enhancing performance on combinatorial optimization algorithms

L'optimitzacio combinatoria es un tipus especific d'optimitzacio matematica on el domini de les variables es discret. Aquest tipus de problemes d'optimitzacio tenen una gran aplicabilitat degut a la seva capacitat d'optimitzacio sobre objectes unitaris i no divisibles. Mes enlla dels algoritmes generics, la comunitat investigadora es molt activa proposant algorismes capacos d'abordar problemes d'optimitzacio combinatoria per a problemes especifics. L'objectiu d'aquesta tesi es investigar com ampliar l'aplicabilitat d'algorismes d'optimitzacio combinatoria que exploten l'estructura dels problemes a resoldre. Ho fem des de la perspectiva del maquinari d'una computadora, perseguint l'explotacio total dels recursos computacionals que ofereix el maquinari actual. Per assolir generalitat treballem amb tres problemes diferents. Primer abordem el problema de generacio d'estructures de la coalicio (CSGP). Trobem que l'algorisme d'ultima generacio es IDP. Proposem un algoritme optimitzat i paral·lel capac de resoldre el CSGP. Aconseguim aixo definint un nou metode per dur a terme l'operacio mes critica -Splitting-, aixi com definint un nou metode per dividir l'operacio de l'algoritme en els diferent subprocessos. A continuacio, estudiem el problema de determinacio del guanyador (WDP) per a les subhastes combinades (CA). Trobem que l'escalabilitat dels solucionadors d'avantguarda es limitada. Mes concretament, mostrem com millorar la resolucio de resultats de relaxacio LP per al WDP en subhastes combinables de gran escala mitjancant l'aplicacio de l'algoritme AD³. A continuacio, contribuim amb una versio optimitzada d'AD³ que tambe es pot executar en un escenari paral·lel de memoria compartida. Finalment, estudiem l'aplicacio de AD³ per resoldre les relaxacions LP d'un problema mes exigent de la computacionalment: El problema de la predicio de cadenes laterals (SCP). Presentem una manera optimitzada de resoldre l'operacio mes critica, la resol·lucio d'un problema quadratic per a un factor arbitrari. En tots els casos proposem algoritmes optimitzats que es poden escalar de forma paral·lela i que milloren notablement l'estat de la tecnica. Tres ordres de magnitud a IDP, i un ordre de magnitud a AD³. L'objectiu final d'aquest treball es demostrar com un disseny algorisme conscient de maquinari pot conduir a millores de rendiment significatives. Mostrem estrategies exportables a algorismes d'optimitzacio combinatoria similars. Aquestes estrategies ajudaran al dissenyador d'algorismes a assolir mes eficiencia en les CPU modernes.
Real-time ekf-based structure from motion

Automated tridimensional scene and egomotion estimation from the only input of a set of images taken by a monocular camera has been a long-term aim in the Computer Vision community since the decade of the eighties. As the result of intense research, the so-called Structure from Motion (SfM) problem has experienced great advances. Automated detection of salient features in natural images and the poste- rior establishment of correspondences along the rest of the images has been one of the rst milestones in this thread of research. Also, geometric relationships between pairs of images have been modeled based on projective geometry concepts including the most general cases; assuming no known information about the scene and including degenerate con gurations and uncalibrated settings. From these relations and a set of matches, relative transformations between pairs of camera poses and scene structure can be estimated. This initial solution can be re ned in a non-linear optimization stage -known as Bundle Adjustment (BA)- in order to obtain a globally consistent estimation. Di erently from this main trend based on pairwise initial estimation fol- lowed by Bundle Adjustment, filtering-based SfM estimation have received considerably less attention and still lacks a thorough analysis of the main geometric issues tackled by SfM research along the latest three decades: projective modeling of the scene, estimation under degenerate con gurations and self-calibration. This thesis' main aim is to assess the efficacy, accu- racy and quality of the filtering-based SfM incorporating all the previously mentioned subjects. Real-time performance at 30 frames per second has been demonstrated for the contributions presented in this thesis, opening the path for real applications. Speci cally, the main contributions in this thesis are 1) the introduction of the projective concepts of low-parallax points and points at in finity in a filtering framework and, based on that, the proposal of a drift-free real-time mosaicing algorithm; 2) a projective coding for 3D points based on explicit inverse depth parametrization, able to cope with low and high parallax con gurations in an undelayed and unifi ed manner; 3) the use of a camera-centered filtering scheme that reduces linearization errors; 4) an efficient 1-Point RANSAC that exploits prior knowledge from filtering; 5) a probabilistic approach to model selection -opposed to the standard one based on geometric reprojection error and regularization terms- well suited to a Bayesian filtering scheme; and 6) a Sum of Gaussians filter that allows accurate full camera self-calibration in a filtering framework.
A Distortion-Resistant Routing Framework For Video Visitors In Wireless

In widespread, traditional routing paths which can be designed for Wi-Fi sensor networks are application-agnostic. we are thinking about a software waft in a wireless sensor networks includes video visitors. to overcome the problem of video distortion there are numerous routing metric mechanisms. Popular hyperlink-best-based routing metrics (etx) do now not account for the dependence of video across the links of a route that may motive a video to glide via few paths, for that reason inflicting excessive video distortion. on this paper, we construct an analytical framework model to evaluate the video frame loss manner to understand the impact of Wi-Fi community now not account for the dependence of video throughout the links of a route, for that reason inflicting excessive on video distortion. This framework lets in to decrease the video distortion by formulating a routing Policy with the aid of accounting the distortions resulting from a go with the flow, stop-to-quit with the aid of dispensing the frames across the paths via precedence primarily based. we evaluate via test bed experiments that our protocol is green in lowering the video distortion and minimizing the user revel in degradation.
Explore One Factor of Affecting Software Reliability Demonstration Testing Result

When the traditional Bayesian method is used to verify software reliability, prior information cannot be distinguished from experimental information. In this paper, a hybrid Bayesian method is used to verify software reliability. At the same time, the correlation coefficient conception is introduced. This paper determines the correlation coefficient in the hybrid beta distribution respectively through the subjective quantitative method, the goodness of fit method, the Kullback information method, the Spearman correlation coefficient method, and the Kendall correlation coefficient method. The feasibility of five kinds of methods for calculating the correlation coefficients is proven by case analysis, and it is also demonstrated that the distinction between prior information and experimental information has an impact on the acceptable quality level of software reliability demonstration testing.
Social Capital and Knowledge Contribution in Online User Communities: One-Way or Two-Way Relationship?

Abstract Firms have been increasingly relying on online user communities to access external, distant knowledge and expertise. Previous research on online user communities has largely investigated the influence of users' social capital on their knowledge sharing behavior. In this study, we propose a two-way relationship between social capital and knowledge contribution of users in online user communities. To test our proposition, we collected and analyzed participation data of 1766 users from the online user community of BMC, a global leader in innovative software solutions. Overall, our study shows evidence of significant bi-directional relationships between social capital (structural, cognitive and relational) and knowledge contribution, while the bi-directional relationships mainly exist among peripheral participants. We discuss the theoretical and practical implications of our study for the online user community literature as well as the broader context of online community.
Ambient Intelligence for Rehabilitation: A Survey

Rehabilitation is defined as a set of measures that assist individuals who experience, or are likely to experience, disability to achieve and maintain optimal functioning in interaction with their environments. Ambient Intelligence (AmI) refers to a digital environment that proactively, but sensibly, supports people in their daily lives. AmI is useful in order to rehabilitate patients. In this paper, a survey of AmI for rehabilitation is presented.
Hunting Traits for Cryptojackers

: Cryptocurrencies are renowned world wide nowadays and they have been adopted in various industries. This great success comes from both the technology innovation they brought to the world, the blockchain, and the ﬁnancial opportunities they opened up for investors. One of the unpleasant aspects are the cybercriminals who took advantage of this technology and have developed malicious software (i.e. cryptojacker) in order to gain proﬁt by mining cryptocurrencies on their victims’ personal computer without any consent. This paper proposes to analyze standalone cryptojackers, both statically and dynamically, with the aim of identifying speciﬁc traits. The approach draws out features speciﬁc to cryptojackers that are selected using statistical methods and explains why a cryptocurrency mining malware has such traits. Based on 20 selected speciﬁc features, three different supervised learning classiﬁcation models have been trained, which are able to differentiate between clean applications and cryptojackers reliably. In experiments, an average accuracy of ≈ 92.46% has been achieved.
4 dof scada-based robotic arm for education in engineering

Robotic Arm has become a large potential tool to teach engineering disciplines in automation control courses. In fact, over the past decades, industrial tasks increasingly rely on robots in a wide range of applications in order to handle tasks that could be difficult and hazardous to human beings. The expanding implementation of supervisory control and robotics systems requires engineers with expertise in electrical design, instrumentation, mechanical design and computer programming for a large number of automation systems. This paper introduces a complete Visual C# and SCADA based software for control, monitoring and simulation of a 4-DOF robotic arm for material handling applications. To further this goal a prototype has been created in a CNC machine and a microcontroller 18F4550 board has been connected to an industrial supervisory. The main context of this study is described as an important tool both to motivate students and to serve as the practical use in industrial simulations. Furthermore, not only can the students program and implement microcontroller software, but students teams can also create a multi-disciplinary hands-on skills ranging from basic concepts of kinematics to design manufacturing plants.
Neutral theory based mutation operator for Differential Evolutionary algorithms to enhance population diversity

As an easily used and powerful heuristic search technique based on population, Differential Evolution (DE) algorithm has been widely applied for various global optimization and real engineering problems. Nevertheless, as with other Evolutionary Algorithms (EA), DE could not avoid from premature convergence due to over concentrated population, which could be called losing population diversity. In order to enhance its performance, we propose a Neutral Mutation (NM) operator for DE algorithm. This novel operator is inspired by neutral theory of molecular evolution, which claims that most mutations at the molecular level are neutral. That is to say, most variations observed are with neither advantage nor disadvantage fitness. Thus, they would not affect an organism’s ability to survive and reproduce. The NM operator maintains slightly deleterious trial vectors, which we called neutral or nearly neutral, with a certain probability in the conventional selection operator of DE. Besides, some of these trial vectors have a chance to be neutrally mutated within the search domain randomly. As a result, the population is diversified with costing negligible Function Evaluations (FEs). Comprehensive experimental results demonstrate that the presented NM operator could improve population diversity to some extent, especially when the population is not divergent at all.
Question answering system supporting vector machine method for hadith domain

Retrieving accurate answers based on users’ query is the main issue of question answering systems. Challenges such as analyse the need of users’ query and extract accurate answers from large corpus are increase the difficulty of developing effective question answering system. This work aims to enhance the accuracy of question answering system for hadiths using useful methods. Pre-processing methods like tokenization and stop-word removal is used to identify the main concepts of users’ query. Answering processing methods and techniques like N-gram, WordNet, CS, and LCS are used to update and enrich the extracted concepts of users’ query based on the formal representation of hadiths answers or documents. Support Vector Machine (SVM) and Name Entity Recognition (NER) methods are conducted to classify Hadiths documents based on relevant subjects and questions types in order to reduce the searching scope of answers documents. Documents in Hadith corpus are classified according to proposed question types, and related subjects as four main classes which are: when for pray, where for pray, when for fasting, and where for fasting. The SVM classification of documents is accomplished supporting NER methods to identify the places (where) and time (when) features that included in the documents. The proposed question answering system is tested using 132 Hadiths documents about Fasting and Pray that are selected from Al-Bukhari source. The findings revealed that the average answers accuracy using CS technique is 67%, the average answers accuracy using LCS technique is 66%, the average answers accuracy using combination of CS and LCS techniques is 70%, and the average answers accuracy using CS, LCS, and SVM is 80%. SVM enhance the system accuracy up to 10% more than using other methods without classification processes. The main contribution of this research is using SVM method to reduce searching scope of Hadiths documents based on various subjects and question types beside effective analysis of query need using NLP methods. SVM provides more accurate answers than extracting answers using only similarity techniques such as CS and LCS.
Knowledge-Augmented Multimodal Deep Regression Bayesian Networks for Emotion Video Tagging

The immanent dependencies between audio and visual modalities extracted from video content and the well-established film grammar (i.e., domain knowledge) are important for emotion video recognition and regression. However, these tools have yet to be exploited successfully. Therefore, we propose a multimodal deep regression Bayesian network (MMDRBN) to capture the relationship between audio and visual modalities for emotion video tagging. We then modify the structure of the MMDRBN to incorporate domain knowledge. A regression Bayesian network (RBN) is formed from one latent layer, one visible layer and directed links from the latent layer to the visible layer. RBN is able to fully represent the data, since it captures the dependencies not only among the visible variables but also among the latent variables given visible variables. For the MMDRBN, first, we learn several layers of RBNs using audio and visual modalities, and then stack these RBNs to form two deep networks. A joint representation is obtained from the top layers of the two deep networks, capturing the deep dependencies between audio and visual modalities. We also summarize the main audio and visual elements used by filmmakers to convey emotions and formulate them as semantical meaningful middle-level representation, i.e., attributes. Through these attributes, we construct the knowledge-augmented MMDRBN, which learns a hybrid middle-level video representation using video data and the summarized attributes. Experimental results of both emotion recognition and regression from videos on the LIRIS-ACCEDE database demonstrate that the proposed model can successfully capture the intrinsic connections between audio and visual modalities, and integrate the middle-level representation learning from video data and semantical attributes summarized from film grammar. Thus, it achieves superior performance on emotion video tagging compared to state-of-the-art methods.
Efficient Secure Floating-point Arithmetic using Shamir Secret Sharing

Successful deployment of privacy preserving collaborative applications, like statistical analysis, benchmarking, and optimizations, requires more efficient secure computation with real numbers. We present a complete family of protocols for secure floating-point arithmetic, constructed using a small set of building blocks that preserve data privacy using well known primitives based on Shamir secret sharing and related cryptographic techniques. Using new building blocks and optimizations and simpler secure fixed-point arithmetic, we obtain floating-point protocols with substantially improved efficiency.
1-D memristor networks as ternary storage cells

Due to its inherent analog nature, the memristor can store information in a continuous form, being thus well-suited for compact multi-bit memory cell technology. In this context, threshold-type switching devices show great retention and switching speed but still poor controllability. To this end, in this work we use one-dimensional (1-D) networks of anti-serially connected threshold-type memristors as means to to create voltage-controlled ternary memristive switches. We demonstrate that the number of memristors and their polarity define the memristance corresponding to the different stored information. We present a simulation-based study of their performance using a threshold-type switching model of bipolar voltage-controlled memristors, and comment on the applied programming-pulse characteristics and the most important device-level properties.
Economic Optimal Dispatch of Grid-connected Microgrid Based on Improved Genetic Algorithm

Nowadays, the demand for energy is expanding, the fossil resources are on the verge of exhaustion, and the ecological environment is getting worse and worse. In this form, this paper studies the economic optimal dispatch of microgrid under the background of energy saving and emission reduction. Firstly, the mathematical model of microgrid composed of distributed power, energy storage devices and loads is established. Secondly, under the background of energy saving and emission reduction, the objective function of multi-objective optimal operation of microgrid is established considering both economic and environmental aspects. Finally, the improved Genetic Algorithm is used to solve the objective function. The results of optimization can reduce microgrid system generation cost.
Formulating better medicines for children.

International Association for Pharmaceutical Technology Register by 15 July 2016 to take advantage of the early bird fee Early bird Full fee Non-Member (Non Academic, Non Governmental) 1225 EUR 1325 EUR Member of APV/EuPFI (Non Academic, Non Governmental) 1095 EUR 1195 EUR Non-Member (Academic, Governmental) 525 EUR 575 EUR Member of APV/EuPFI (Academic, Governmental) 460 EUR 510 EUR Students (please enclose evidence) 230 EUR 270 EUR Workshop prices additional to registration fee: Non-Member + Member APV/EuPFI (Non-Academic, Non-Governmental) 330 EUR 380 EUR Non-Member + Member of APV/EuPFI (Academic, Governmental) 180 EUR 205 EUR Students (please enclose evidence) 90 EUR 115 EUR