Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Control of Nonlinear Multibody Flexible Space Structures

ROBUST CONTROL OF NONLINEAR MULTIBODY FLEXIBLE SPACE STRUCTURES Atul G. Kelkar Department of Mechanical Engineering and Mechanics Old Dominion University Director: Dr. Thomas E. Alberts Co-Director: Dr. Suresh M. Joshi A generic nonlinear math model of a multibody flexible system is developed. Asymptotic stability of such systems using dissipative compensators is established. It is proved that, under certain conditions, this class of systems exhibit global asymptotic stability under dissipative compensation. The dissipative compensators considered are static as well as dynamic dissipative compensators. The stability proofs are based on passivity approaches, Lyapunov methods, as well as a key property of such systems, i.e., skew-symmetricity of certain matrix. The importance of the stability results obtained is that the stability is robust to parametric uncertainties and modeling errors. For static dissipative compensators, it is shown that stability is not only robust to parametric uncertainties and modeling errors but also to certain actua­ tor and sensor nonlinearities. Actuator nonlinearities considered are (0, oo) sector R eproduced with perm ission of the copyright owner. Further reproduction prohibited without perm ission. monotonically non-decreasing type, which include realistic nonlinearities such as the saturation nonlinearity. In the presence of dead-zone and hysteresis type nonlineari­ ties, system trajectories do not approach equilibrium point asymptotically, however, it is shown that there is a compact region of ultimate boundedness and system tra­ jectories do not go unbounded. The sensor nonlinearities considered are (0, oo) sector nonlinearities. A more versatile class of dissipative compensators, called dynamic dissipa­ tive compensators, is next considered. A control designer has more design freedom with dynamic dissipative compensators than with the static dissipative type. The increased design degrees of freedom can be used to enhance the performance of the control system. The synthesis techniques for static as well as dynamic dissipative compen­ sators for multibody, nonlinear, flexible systems are currently unknown and it is a topic of future research. The asymptotic stability property of a static dissipative controller for multibody, nonlinear, flexible space structures is demonstrated through a simulation example. The example system used consists of a flexible 10-bay truss structure with a flexible, 2-link manipulator arm attached at one end of the truss. This example system is representative of the class of spacecraft envisioned for the future, missions. For dynamic dissipative compensators an application example is shown for a multibody planar system with an articulated member. The controller de­ sign is based on locally linearized models in the configuration space of the articulated member. This example also demonstrates the use of dissipative compensators in the integrated design framework. R eproduced with perm ission of the copyright owner. Further reproduction prohibited without perm ission.
Beyond Google’s PageRank: Complex Number-based Calculations for Node Ranking

This study is focused on a proposed alternative algorithm for Google's PageRank, named Hermitian centrality score, which employs complex numbers for scoring a node of the network to overcome the issues of PageRank’s link analysis. This study presents the Hermitian centrality score as a solution for the problems of PageRank, which are associated with the damping factor of Google’s algorithm. The algorithm for Hermitian centrality score is designed to be free from a damping factor, and it reproduces PageRank results well. Moreover, the proposed algorithm can mathematically and systematically change the point of a node of a network.
The Selection of Best Employee by using Analytical Hierarchy Process Method in Go Wet Water Park

In a company, human resource is something that must really be considered, the company must have good quality of human resources and be able to work well so that the company's sustainability can be maintained. To maintain the quality and existence of its employees, the company needs to give appreciation to its employees so that employees are motivated to work better. Employees are not motivated to work better because there is no model employee selection yet. Selecting and determining model employees requires a method or decision support system to support the selection of model employees. There are many methods that can be used to select model employees, one of which is the Analytical Hierarchy Process method. To support the selection of model employees, software that can make it easier to determine model employees is needed. The software used is Expert Choice 2000 software. The results obtained from research on the selection of model employees using the Analytical Hierarchy Process method in Go Wet Water Park are employees named Nugraha successfully selected as a model employee in the company with an overall inconsistency of 0.1.
Design and Analysis of Probabilistic Shaping Scheme for Uplink Nonorthogonal Multiple Access Systems

Probabilistic shaping (PS) combined with bipolar amplitude shift keying modulation is a promising transmission scheme to increase spectral efficiency. Different from the existing design of the point to point communications, we propose a PS-based uplink nonorthogonal multiple access (NOMA) scheme for fading channels to support multiple connections simultaneously, where successive interference cancellation (SIC)-based maximum a posterior (MAP) detection is implemented at the receiver. To exploit the shaping gain for each user, we first theoretically analyze the ergodic channel capacity and the constellation constrained (CC) capacity with discrete-constellation inputs, respectively. And then, a multi-step optimization scheme for maximizing the ergodic CC capacity is proposed to obtain the optimal probability mass function (PMF) of these input signals. The order of optimization is inverse to the SIC decoding order so that for each specific user, the remaining multiuser interference is calculated based on the optimal PMF of the later decoded users. Furthermore, based on this non-equiprobable transmission scheme, the closed-form pairwise error probability expressions for each user are obtained for the first time. Finally, compared with the uniform scheme, the simulations show that the proposed PS NOMA strategies achieve more than 1 dB gain in terms of the error performance.
Effectiveness of Virtual Reality in the Motivational Processes of Learners

This brief article reports on the use of virtual reality and its effectiveness on improving and maintaining learners' intrinsic motivation or interest. Research suggests that interest contributes to learning. Therefore a study of the impact of interests is essential to an understanding of intrinsic motivation. Since the virtual environment provides a sense of presence, it may be possible to create scenarios to stimulate the learners' curiosity and interest. Eighteen students, 11 males and 7 females, between 21 and 32 years old, served as subjects for the study. The experiment consisted of the physical world environment using wooden blocks, and the virtual world using virtual blocks. Both worlds used color and shape as variables. The two variables consisted of three shapes (sphere, pyramid, and cube), and three colors (red, green, and blue). In both worlds, the wooden blocks and virtual blocks had to be manipulated and arranged in nine different patterns. The first experiment started with a two-block pattern. At each step the difficulty was increased by increasing the number of blocks. The subject's score was based on a ten-point scale instrument administered at the end of each experiment. The scores ranged from very weak to very strong. The results were used to identify a significant difference between the subjects' performance in the virtual world and in physical world with respect to curiosity, interest, and sense of control. The interest level comparison indicated that for all subjects scores in the virtual world were always higher than the scores in the physical world. The sense of control level comparison indicated that in the beginning scores in the virtualworld were not always higher than the scores in the physical world. However, after orientation to navigation through the virtual environment, the mean score gradually rose. This research demonstrates that the virtual world is more useful than the physical world (with respect of color and shape) in increasing the memory span of the learner.
IoT Load Classification and Anomaly Warning in ELV DC Picogrids Using Hierarchical Extended ${k}$ -Nearest Neighbors

The remote monitoring of electrical systems has progressed beyond the need of knowing how much energy is consumed. As the maintenance procedure has evolved from reactive to preventive to predictive, there is a growing demand to know what appliances reside in the circuit (classification) and a need to know whether any appliance requires attention and maintenance (anomaly warning). Targeting at the increasing penetration of dc appliances and equipment in households and offices, the described low-cost solution consists of multiple distributed slave meters with a single master computer for extra low voltage dc picogrids. The slave meter acquires the current and voltage waveform from the cable of interest, conditions the data, and extracts four features per window block that are sent remotely to the master computer. The proposed solution uses a hierarchical extended <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>-nearest neighbors (HE-<inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>NNs) technique that exploits the use of distance in <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>NN algorithm and considers a window block instead of individual data point for classification and anomaly warning to trigger the attention of the user. This solution can be used as an <italic>ad hoc</italic> standalone investigation of suspicious circuit or further expanded to several circuits in a building or vicinity to monitor the network. The solution can also be implemented as part of an Internet of Things application. This article presents the successful implementation of the HE-<inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>NN technique in three different circuits: 1) lighting; 2) air-conditioning; and 3) multiple load dc picogrids with accuracy of over 93%. Its performance is superior over other anomaly warning techniques with the same set of data.
Oxidative Stress and Exercise in Cardiopulmonary and Metabolic Disorders 168

Oxidative stress appears to be involved in the pathophysiology of multiple human diseases. Although various stimuli exist, acute bouts of strenuous exercise are known toinduceatransientincreaseinreactiveoxygenandnitrogenspecies(RONS),evident by numerous reports of increased oxidative stress biomarkers following single bouts
Improving the Efficiency of SVM Classification With FHE

In an ever more data-centric economy, machine learning models have risen in importance. With the large amounts of data companies collect, they are able to develop highly accurate models to predict the behaviours of their customers. It is thus important to safeguard the data used to build these models to prevent competitors from mimicking their services. In addition, as this type of techniques finds its way into areas that need to deal with more sensitive information, like the medical industry, the privacy of the data that needs to be classified also has to be ensured. Herein, this topic is addressed by homomorphically evaluating Support Vector Machine (SVM) models, in a way that guarantees that a client learns nothing about the model except for the classification of his data, and that the service provider learns nothing about the data. Whereas, previously, Fully Homomorphic Encryption (FHE) has mostly focused on either bit-wise or value-wise computations, SVMs present an additional challenge since they combine both: during an initial phase a kernel function is evaluated that makes use of real arithmetic, and during a second phase the sign bit has to be extracted. Novel techniques are herein proposed that allow for speedups of up to 2.7 and 6.6 for the evaluation of polynomials and the determination of sign, respectively, in comparison to the state of the art. Finally, it is shown that the proposed techniques do not deteriorate the classification accuracy of the SVM models.
Privacy preserving data storage procedures productively for cloud computing over mobile networks.

Distributed computing is a promising innovation, which is changing the conventional Internet registering worldview and IT industry. With the advancement of remote access innovations, distributed computing is required to grow to versatile conditions, where cell phones and sensors are utilized as the data gathering hubs for the cloud. Notwithstanding, clients' worries about information security are the primary snags that block distributed computing from being broadly received. These worries are begun from the way that touchy information lives in open mists, which are worked by business specialist organizations that are not trusted by the information proprietor. In this manner, new secure administration structures are expected to address the security worries of clients for utilizing cloud figuring strategies. In this project, we present a comprehensive security structure to secure the information stockpiling in open mists with the uncommon core interest on lightweight remote gadgets store and recover information without uncovering the information substance to the cloud specialist organizations.
Core‐sets: An updated survey

In optimization or machine learning problems we are given a set of items, usually points in some metric space, and the goal is to minimize or maximize an objective function over some space of candidate solutions. For example, in clustering problems, the input is a set of points in some metric space, and a common goal is to compute a set of centers in some other space (points, lines) that will minimize the sum of distances to these points. In database queries, we may need to compute such a some for a specific query set of k centers. However, traditional algorithms cannot handle modern systems that require parallel real‐time computations of infinite distributed streams from sensors such as GPS, audio or video that arrive to a cloud, or networks of weaker devices such as smartphones or robots. Core‐set is a “small data” summarization of the input “big data,” where every possible query has approximately the same answer on both data sets. Generic techniques enable efficient coreset maintenance of streaming, distributed and dynamic data. Traditional algorithms can then be applied on these coresets to maintain the approximated optimal solutions. The challenge is to design coresets with provable tradeoff between their size and approximation error. This survey summarizes such constructions in a retrospective way, that aims to unified and simplify the state‐of‐the‐art.
Optimal Fault Diagnosis of Electric Power Systems Using MSE Learning

The electric power systems (EPSs) is a complex system with lots of bus bars, transmission lines, and transformers. The primary goal of maintaining an EPS is providing sustainable and stable power supply for customers. However, the quality of service of an EPS suffers from the failures of its system sections. Fast and accurate fault diagnosis is the prerequisite to bring the system back in normal state, thus has attracted much attentions from power engineers. In this paper, we present a minimum square error (MSE) learning based optimal fault diagnosis algorithm, in which the operation state of system sections and protections are formulated using matrices representations. Optimization model is developed to find most probably state of the system sections. The method requires no complex logic designing, nor any historical operation records, and it is simple and fast for implementation. Test results prove that the proposed method has satisfactory diagnosis performance compared with other existing methods.
Automatic Repair of Timestamp Comparisons

Automated program repair has the potential to reduce the developers’ effort to fix errors in their code. In particular, modern programming languages, such as Java, C, and C#, represent time as integer variables that suffer from integer overflow, introducing subtle errors that are hard to discover and repair. Recent researches on automated program repair rely on test cases to discover failures to correct, making them suitable only for regression errors. We propose a new strategy to automatically repair programs that suffer from timestamp overflows that are manifested in comparison expressions. It unifies the benefits of static analysis and automatic program repair avoiding dependency on testing to identify and correct defected code. Our approach performs an abstract analysis over the time domain of a program using a Time Type System to identify the problematic comparison expressions. The repairing strategy rewrites the timestamp comparisons exploiting the binary representation of machine numbers to correct the code. We have validated the applicability of our approach with 20 open source Java projects. The results show that it is able to correctly repair all 246 identified errors. To further validate the reliability of our approach, we have proved the soundness of both, type system and repairing strategy. Furthermore, several patches for three open source projects have been acknowledged and accepted by their developers.
Formal Methods for Security

At first sight – and even at second sight – security looks like an interesting application area for formal methods. One reason for this is that security flaws have a higher impact than more harmless bugs. This might justify or even require the extra effort to invest in using formal methods. Indeed, the highest levels of certification using the Common Criteria security evaluation standard require the use of formal methods. Another reason is that some security problems are orthogonal to – or at least largely independent of – the functionality of a system. Indeed, security vulnerabilities can be seen as ‘anti-functionality’: functionality that is unintentionally provided, also to attackers, which should not be available at all. Given that writing complete functional specifications is hard, and totally unfeasible in most cases, concentrating on partial specs that ensure some generic safety properties problems might provide a better return of investment, also because such specs could be re-used across applications. Unfortunately, things are not so simple. Security properties can be tricky to specify. Indeed, attackers can be very creative in finding and exploiting new loopholes. A common and natural way to specify security properties is in a ‘negative’ way, by saying that something – some type of attack – is not supposed to be possible. For example, a web application should not be vulnerable to SQL injection or XSS. List of these negative properties are useful in testing, as they suggest negative tests (i.e. test-cases which are supposed to fail, by triggering some error response), but they are not immediately helpful in construction. Moreover, these lists of negative properties are typically incomplete. They only address a limited set of known potential problems, not all potential problems. Moreover, sometimes security problems arise because it turns out fundamental assumptions about programs can be broken by an attacker, invalidating the very abstractions that we use in formal methods to reason about programs. Classic examples here are fault attacks (for example the Rowhammer attack to flip some bits in DRAM memory) or information
TraceChain: A blockchain‐based scheme to protect data confidentiality and traceability

The risk of sharing data in cloud computing has gathered increasing attention. After the owner of some confidential data outsources the data to cloud storage services and shares it with others, the data owner lost the control to the data to a large extent. To achieve data sharing while keeping data confidentiality, attribute‐based encryption (ABE) can be employed by cloud storage services. However, ABE can only guarantee that outsourced data on the cloud is decrypted by attribute‐satisfying users but cannot restrict data from being accessed by dishonest users whose attributes also satisfy the access‐control policy. It is impossible for the data owner to control the shared data after it has been decrypted by dishonest users, especially when a set of attribute‐satisfying dishonest users may collude. To address this concern, we propose a traceable data sharing scheme called TraceChain. In TraceChain, data is encrypted over a new CP‐ABE scheme called E‐CP‐ABE. Furthermore, the system parameters for generating the private key in E‐CP‐ABE are uploaded to the private blockchain and transactions are performed on the chain. The data owner can obtain the identity of users by monitoring system parameters simultaneously and control data sharing on the blockchain. To prove the security of our scheme, the security analysis is given in this paper. Meanwhile, experimental results also show that our system is viable and efficient.
Contribution to Panoptic Segmentation

Full visual scene understanding has always been one of the main goals of machine perception. The ability to describe the components of a scene using only information taken by a digital camera has been the main focus of computer vision tasks such as semantic segmentation and instance segmentation. Where by using Deep Learning techniques, a neural network is capable to assign a label to each pixel of an image (semantic segmentation) or define the boundaries of an instance or object with more precision than a bounding box (instance segmentation). The task of Panoptic Segmentation tries to achieve a full scene description by merging semantic and instance segmentation information and leveraging the strengths of these two tasks. On this report it is shown a possible alternative to solve this merging problem by using Convolutional Neural Networks (CNNs) to refine the boundaries between each class.
Analysis of Non-Orthogonal Sequences for Grant-Free RA With Massive MIMO

Massive MIMO based grant-free random access (mGFRA) is a promising RA technique for massive access with low signaling overhead. However, due to a limited orthogonal preamble size, preamble collision is a key factor that constrains the success probability of mGFRA. In this paper, we examine the potential of applying non-orthogonal preambles to mitigate the issue, where two typical non-orthogonal sequences, i.e., Gaussian distribution sequences and Zadoff-Chu (ZC) sequences, are taken into account. Asymptotic behaviors of the success probabilities with the non-orthogonal preamble sequences are analyzed and compared using derived closed-forms. Through the analysis we reveal that ZC sequences outperform Gaussian ones. However, though non-orthogonal sequences are able to reduce or even alleviate the preamble collision, they do not necessarily provide better performance than the orthogonal counterpart. In addition, regarding orthogonal sequences as a single-root case of ZC sequences, we show that there exists an optimal number of ZC roots that maximizes the success probability for mGFRA, which is low-bounded by orthogonal baseline. Simulation results evaluate the performance of non-orthogonal sequences in mGFRA under various practical system parameters, and validate our analysis.