Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient parameter identification and model selection in nonlinear dynamical systems via sparse Bayesian learning

Bayesian inference plays a central role in many of today’s system identification algorithms. However, one of its major drawbacks is that it often requires solutions to analytically intractable integrals, and this has led to solutions via Markov Chain Monte Carlo (MCMC). Today, few Bayesian system identification methods can compete with the robustness of the family of MCMC solutions. However, MCMC suffers severely from its computational cost. Partly fuelled by the field of Compressive Sensing (CS), an interest in the machine learning community has arisen in sparse linear regression. Its value in identification of dynamical systems has also recently started to receive some attention. The idea is to represent the range of possible candidate functional forms that have generated a specific data set using a dictionary, and to then apply standard Lasso regression to select the “best” basis, with sparsity constraints. A major problem in this approach is that Lasso regression (and its derivatives) requires tuning of the regularisation parameter. In this paper, a procedure for selecting the “best basis” (and thus performing both model selection and system identification) is presented through a sparse Bayesian learning approach. Sparsity is induced via a hierarchical Gaussian prior and an approximation to the posterior distribution is sought using an iterative optimisation scheme for finding the optimal hyper-priors that govern prior and hence, the level of sparsity in the solution. The method is applied to five systems of engineering interest, which include a baseline linear system, an additive quadratic damping term, cubic stiffness (Duffing oscillator), Coulomb damping and a Bouc-Wen hysteresis model. The results are shown using numerical simulations. It is shown that this approach can identify not only the correct model parameters, but whether a nonlinearity is present in the system as well its type. With the formulation being Bayesian, it also yields estimates of uncertainty over the selected basis functions and predicted responses.
Intraday Residential Demand Response Scheme Based on Peer-to-Peer Energy Trading

The intermittency introduced by the increasing integration of distributed renewable energy sources is challenging the efficient operation of residential distribution systems. A promising solution to tackle this challenge is the implementation of residential demand response through responsive household appliances such as heat pumps, refrigeration devices, and energy storage units. In this article, a peer-to-peer energy trading platform among residential houses is proposed to coordinate demand response schemes and level off potential generation/consumption disturbances in the hour-ahead intraday context. First, the day-ahead and intraday energy management models for residential houses are established considering the characteristics of responsive household appliances and energy storages. The discomfort and possible economic losses for performing demand responses are quantified with respect to the risk preferences of residential customers. The peer-to-peer energy trading platform is developed and a double-auction mechanism employed to promote the collaborative demand response schemes in the face of disturbances. An optimal bidding strategy of residential houses is also proposed. The feasibility of the proposed models and bidding strategy are verified through case studies. It is also illustrated that the residential demand response schemes and intraday peer-to-peer energy trading are effective in managing the uncertainties of load demand and renewable generation.
The Incorruptible Integrator: A Streamlined Approach to IMC-PID Controller Tuning

In automakers’ never-ending quest to reduce emissions and improve performance, the turbocharger represents a major step in advancing these goals. By repurposing waste exhaust and compressing the air intake, they are able to increase overall power. One critical control loop in the turbocharger is control of boost pressure via the wastegate. This is a highly nonlinear process and experimental data has shown that a gain-scheduled PID (proportional integral derivative) controller developed with IMC (internal model control) tuning methodology is an effective means to control boost pressure. Motivated by this successful implementation of IMC-PID tuning in the automotive world, this work hopes to extend and analyze that framework. Traditionally, the success of an IMC controller depends on the accuracy of the plant model. This research challenges this view and investigates using IMC with a gain-integratordelay (GID) model identified at a critical frequency, regardless of the actual plant. The GID model is useful because of its simplicity to characterize and its ability to be translated to the ubiquitous PID controller easily. Three design techniques are developed: (1) design for post-hoc tuning, (2) design for closed loop bandwidth, and (3) design for phase margin. In addition, these techniques are investigated via a Monte Carlo simulation to determine efficacy for when there exists plant/model mismatch. Finally, the three techniques are applied to control the speed of an inertia disk on the Quanser QUBETM Servo 2 device.
Integrating Environmental Management Control Systems to Translate Environmental Strategy into Managerial Performance

Purpose This study aims to investigate the role of environmental management control systems as mechanisms to translate environmental strategy into environmental managerial performance. Design/methodology/approach Based on survey data from 218 firms, the authors test a structural equation model. Findings The results show that environmental management control systems mediate the relationship between environmental strategy and environmental managerial performance. Moreover, the level of integration between regular and environmental management control systems significantly impacts the relationship between environmental management control systems and environmental managerial performance. Therefore, environmental management control systems are important mechanisms to translate environmental strategy into managerial performance, and a high level of integration can reinforce this role. Research limitations/implications The typical shortcomings of survey-based research apply to this study. Originality/value While previous research focuses primarily on environmental performance at the organizational level, this study addresses individual managerial performance with regard to environmental outcomes. In addition, the authors investigate how the level of integration between regular and environmental management control systems influences the relationship between environmental strategy and environmental managerial performance as well as the mediating role of environmental management control systems.
Improved BAQ algorithm for Tiangong‐2 interferometric imaging radar data compression

Tiangong-2 interferometric imaging radar altimeter (InIRA), the first space-borne radar altimeter in the world, applying a new mechanism of interferometry with short baseline and small incident angle, can achieve wide swath and high accuracy sea surface height measuring. Comparing to traditional altimeter, InIRA can obtain SAR image of observation area, which means InIRA needs higher AD sampling rate. Due to the limitation of satellite on-board storage and downlink speed, in order to extend the working time of InIRA, data compression algorithm for InIRA must be studied. Block adaptive quantisation (BAQ) is an efficient and widely adopted approach for space-borne SAR raw data compression. Based on the characters of InIRA echo waveform, this study discusses compressing InIRA raw data by changing quantisation bits and size of blocks of BAQ algorithm to achieve higher compression ratio and less sea surface height error.
Research on Answer Extraction for Automatic Question Answering System

The question answering system as a hot issue in the field of NLP study, and answer extraction as the core part of the automatic question answering system, its effect is good or bad is directly related to the performance of question answering system, this paper introduces the technical process of answer extraction module and the use of pattern matching technology, gradually shift from the traditional pattern matching technology to the machine learning method of pattern matching technology, and also calculate the questions and answers in the answer extraction module are several methods of similarity. Since the performance of the automatic question answering system depends on the algorithm used for answer extraction to a large extent, the selection of the algorithm for answer extraction is also crucial, and the accuracy of the answer returned to the user can be improved by improving the algorithm.
Telescope Array FD Weather Classification using Machine Learning

Telescope Array (TA) cosmic ray observatory fluorescence detector (FD) sites have passed 10 years of operation. In order to select nights for further analysis, clear nights should be distinguished from cloudy or noisy nights in the FD data. Weather observations during the night are recorded every hour while the FDs are collecting data by on-site operators at the Middle Drum site. However a more robust and uniform weather classification method is desired for flagging bad weather in the FD data. A series of snapshots of the night sky was created using the detector’s photomultiplier tubes (PMTs) pedestals. We classified the night’s weather using the PMTs pedestal snapshots using a Recurrent Convolution Neural Network (RCNN).
3. Threat analysis and attack modeling for machine-to-machine communication toward Internet of things

The wide variety of Internet of thing (IoT) applications demands a secure and efficient communication channel that resists against a variety of modern attacks and fulfils application requirement. There are various IoT threats and challenges that must be addressed to make a communication secure in IoT. As growth in devices increases with their potential misuse, so there is a need to integrate security features into the available IoT algorithms and protocols. At the same time, we need to design new IoT protocols and algorithms for extended features of machine-to-machine (M2M) communication. This chapter has proposed a protocol stack that is mapped to the Transmission Control Protocol/Internet Protocol (TCP/IP) which is used for communication and connection of network devices on internet. The protocol present in a particular layer must acquire a security feature of associated layer. This chapter also elaborates on M2M communication in IoT. This chapter presents and discusses the literature survey corresponding to access control mechanism and trust management policies. From the literature survey it is observed that the existing security protocols, methods and algorithms are not suitable for IoT resource-constrained environment. The various resource-constrained devices are unable to cope up with different attacks. This chapter gives detailed analysis of attacks with its behavioral modeling. If the reader understands how attacks happen, then it will be easy to investigate solutions. From the extensive literature survey this chapter has drawn gap analysis, which gives insight into security requirements of IoT protocols and security algorithm. In addition, the chapter proposes a novel security framework that emphasizes on making secure communication layer with the help of trust management policies, distributed access control framework and privacy-aware protocols.
Near-Optimal Detection of Monobit Digitized UWB Signals in the Presence of Noise and Strong Intersymbol Interference

Ultra-wideband (UWB) channels include many multipath components and thereby introduce a strong intersymbol interference (ISI) for high-rate communications. Hence, in such cases, a high-performance low-complexity detection is required to cancel ISI. Since monobit analog-to-digital converters (ADCs) are simple and consume less energy, they are increasingly used in UWB communication systems. However, since monobit digitization is a nonlinear operation, ISI removal after monobit digitization becomes a difficult task. In this paper, a near-optimal two-stage algorithm is proposed to iteratively detect the noise-and-ISI-contaminated monobit digitized UWB signals. Moreover, a novel theoretical framework for bit-error-rate (BER) evaluation is developed which guarantees a real-valued BER and doubles the speed of the numerical calculations by using the conjugate (Hermitian) symmetric property of the characteristic function. Computer simulations have been employed to validate the theoretical analysis and the approximations used therein. Both simulation and theoretical results show that the monobit receiver employing the proposed algorithm only incurs about 0.9 dB performance loss compared to the optimal monobit receiver. Moreover, the resistance of the proposed algorithm to error propagation is validated.
Direct Power Control of a Doubly Fed Induction Generator in Wind Power Systems

The doubly fed induction generator (DFIG), due to its improved dynamic behavior, reduction of mechanical stress on wind turbine (WT) and the increase of power capture, is the most popular for large scale wind energy generation systems. Since DFIG is a wound rotor induction machine, most well-known schemes for induction motor drives such as field oriented control (FOC), and direct torque control (DTC) are applicable for controlling the generator in a DFIG-based wind energy system. This dissertation presents the analysis, modeling, simulation and implementation of a direct power control (DPC) for a doubly fed induction generator (DFIG) based wind energy system. Both theoretical and practical aspects of non-linearity behavior of the hysteresisbased controller (HBC) in DPC during grid-connected (GC) modes are investigated. Viability of DPC approach with hysteresis-based controller for stand-alone (SA) mode and transition from SA to GC of DFIG-based wind energy system is also analyzed. Two major drawbacks of HBC on DPC drive, namely high active and reactive power ripples and variable switching frequency of the rotor side converter were studied. In order to address these problems, this dissertation proposed a pair of active and reactive power controllers to replace the hysteresis-based controllers. The active power controller proposed in this thesis consists of a proportional-integrator (PI) controller, two triangular carrier generators and a pair of comparators that produce a three-level output, namely –1, 0, and 1.
Representation of UML class diagrams in OWL 2 on the background of domain ontologies

Background: UML class diagrams can be automatically validated if they are compliant with a domain knowledge specified in a selected OWL 2 domain ontology. The method requires translation of the diagrams into their OWL 2 representation. Aim: The aim of this paper is to present transformation and verification rules of UML class diagrams to their OWL 2 representation. Method: The analysis of the results of the systematic literature review on the topic of transformation rules between elements of UML class diagrams and OWL 2 constructs. The purpose of the analysis is to present the extent to which state-of-the-art transformation rules cover the semantics expressed in class diagrams. On the basis of the analysis, new transformation rules expressing the semantics not yet covered but expected from the point of view of domain modelling pragmatics have been defined. Results: The first result is the revision and extension of the transformation rules identified in the literature. The second original result is a proposition of verification rules necessary to check if a UML class diagram is compliant with the OWL 2 domain ontology. Conclusion: The proposed transformations can be used for automatic validation of compliance of UML class diagrams with respect to OWL 2 domain ontologies.
THE PREDICTING SUCCESS FACTORS OF MOBILE APPLICATIONS DEVELOPMENT: ORGANIZATIONAL PERSPECTIVES

Mobile applications (mobile apps) development is one of the fastest evolving technological innovations across the globe. The immense growth of mobile apps development can be attributed to the incredible rate of growth of mobile technology and its propagation into every facet of modern life. The success of these applications lies within some predicting success factors with respect to stakeholders' expectations and perceptions. Researchers have attempted to identify the factors influencing mobile apps development. However, these researches were mostly conducted from the individual software developers' perspective. This paper presents an empirical investigation of the significance of these predicting factors of mobile apps development from different organizations’ perspectives. A survey was conducted among different organizations that are involved in one way or another in the development of mobile apps. The findings showed that individual development skills, functionality, mobile devices specifications, use of a software development kit, back-end integration, portability, web and network integration, and the use of a system development methodology are the predicting success factors of mobile apps development whereas open source technology is not. Furthermore, different types of organizations have different perceptions of how important a predicting success factor is in the development of mobile apps.
Smooth Transitioning Between two Walking Metaphors for Virtual Reality Applications

Virtual navigation should be as similar as possible to how we move in the real world, however the limitations of hardware and physical space make this a challenging problem. Tracking natural walk is only feasible when the dimensions of the virtual environment match those of the real world. The problem of most navigation techniques is that they produce motion sickness because the optical flow observed does not match the vestibular and proprioceptive information that appears during real physical movement. Walk in place is a technique that can successfully reduce motion sickness without losing presence in the virtual environment. It is suitable for navigating in a very large virtual environment but it is not usually needed in small virtual spaces. Most current work focuses on one specific navigation metaphor, however in our experience we have observed that if users are given the possibility to use walk in place for large distances, they tend to switch to normal walk when they are in a confined virtual area (such as a small room). Therefore, in this paper we present our ongoing work to seamlessly switch between two navigation metaphors based on leg and head tracking to achieve a more intuitive and natural virtual navigation.
A Secure, Reliable and Performance-Enhancing Storage Architecture Integrating Local and Cloud-Based Storage

A Secure, Reliable and Performance-Enhancing Storage Architecture Integrating Local and Cloud-Based Storage Christopher Glenn Hansen Department of Electrical and Computer Engineering, BYU Master of Science The constant evolution of new varieties of computing systems cloud computing, mobile devices, and Internet of Things, to name a few have necessitated a growing need for highly reliable, available, secure, and high-performing storage systems. While CPU performance has typically scaled with Moore’s Law, data storage is much less consistent in how quickly performance increases over time. One method of improving storage performance is through the use of special storage architectures. Such architectures often include redundant arrays of independent disks (RAID). RAID provides a meaningful way to increase storage performance on a variety of levels, some higher-performing than others. The fastest performing arrays, however, come at the expense of data reliability, preventing their use in many applications. In this thesis, a storage architecture is presented which utilizes the highest performing RAID architecture (RAID 0) combined with highly available cloud storage in order to maximize storage performance and reliability, while providing an additional benefit of security that is not currently provided by similar storage architectures. Various levels of RAID and the current field of RAID and RAID-like technologies are outlined. Furthermore, the benefits of combining local and cloud storage in RAID configurations, including the ability to recover data in the event of a simultaneous failure of all local disks within an array, are discussed. Reliability calculations are performed, and a 3x Mean Time to Failure (MTTF) improvement over existing systems is shown. The MTTF, privacy, read/write throughput, and usable storage percentage improvements of local+cloud-RAID 4 and local+cloud-RAID-DP are discussed. Thereafter, a software implementation of local+cloud-RAID 4 is presented and discussed, allowing for notable benefits over existing architectures in terms of reliability and privacy while maintaining performance. Finally, a novel hardware-based local+cloud-RAID controller architecture is described and discussed.
QoE-Driven Multi-User Video Transmission Over SM-NOMA Integrated Systems

To cope with rapid growth of video traffic and energy consumption in mobile networks, we propose a spatial modulation (SM) and non-orthogonal multiple access (NOMA) integrated system for multi-user video transmission (SNIS-MUVT). SNIS-MUVT attempts to exploit the benefits from SM and NOMA to achieve high energy efficiency and high throughput. The main contribution of SNIS-MUVT lies in cross-layer design for video-aware transmission and quality of experience (QoE)-driven power allocation. With scalable video coding, base layers (BLs) are transmitted in the spatial domain while enhancement layers (ELs) are transmitted in the signal domain. In the spatial domain, layered SM is performed to support multi-user transmission according to the required BL rates and channel conditions of different users. In the signal domain, ELs are first extracted based on the estimated channel capacity. Then, the extracted ELs are handled by unequal error protection according to their importance before superposed for NOMA transmission. We formulate the QoE-driven power allocation problem in SNIS-MUVT. Both optimal algorithm based on the hidden monotonicity of the problem and suboptimal iterative algorithm with polynomial time are proposed. Finally, simulation results demonstrate that the proposed SNIS-MUVT outperforms existing SM and SM-NOMA integrated schemes in terms of QoE under various scenarios.
The Research of Techniques in Content-based Image Retrieval

Image feature extraction is the realization of content-based image retrieval based on the first step, but also is the most critical step. Image feature extraction is suitable or not directly affect the performance of image retrieval system, this paper describes the features of the image, and construct a feature-based similarity metric matrix. By using these matrix combinations, a kernel based image similarity measurement matrix is constructed, which lays the foundation for the clustering processing of similarity matrix.