Miracle-dz/UniFAR-FTUs huggingface dataset:
Transmission scheduling for mixed-critical multi-user multiple-input and multiple-output industrial cyber-physical systems

Wireless sensor networks are widely used in industrial cyber-physical system installations, where high reliability and the need for real-time data are the two main characteristics. A large amount of real-time data can be transmitted to its destination on time using a reasonable periodic allocation of a node’s transmission slots. However, a flow may miss its deadline when flow conflicts occur. When such missed deadlines occur regularly, system performance may degrade, and when the flow is critical, such data losses can result in errors or cause disasters. To address this issue, we introduce multi-user multiple-input and multiple-output technology and a mixed-critical system into an industrial cyber-physical system. When an error occurs or when demand changes, the multi-user multiple-input and multiple-output nodes can switch their transmission mode, changing to a high-criticality configuration to meet the system’s new needs. Hence, we first propose a heterogeneous multi-user multiple-input and multiple-output system model. Based on this model, we propose a slot analyzing algorithm that guarantees system schedulability by reallocating slots for each node after replacing conflict nodes with multi-user multiple-input and multiple-output nodes. By considering both system schedulability and cost, the slot analyzing algorithm also reduces the number of multi-user multiple-input and multiple-output nodes required. Then, to further reduce the number of multi-user multiple-input and multiple-output nodes in an industrial cyber-physical system, we propose a priority inversion algorithm that improves schedulability by adjusting slot allocations before replacing conflict nodes with multi-user multiple-input and multiple-output nodes. By reducing the use of multi-user multiple-input and multiple-output nodes, the priority inversion algorithm achieves better performance than the slot analyzing algorithm when the system is in a high-criticality mode. Evaluation results show the effectiveness and efficacy of our approaches.
Model Integration and Traceability for Product Service Systems Engineering

PSS engineering is a complex enterprise that brings together various domains, such as product, software and service engineering. To ensure requirements traceability in PSS engineering one needs to take into account the special characteristics and complexities that are relevant in this context. In this thesis, we developed a traceability and model integration solution for PSS engineering consisting of a model integration ontology and software tool supporting traceability and model integration.
Rapid Transformer Health State Recognition Through Canopy Cluster-Merging of Dissolved Gas Data in High-Dimensional Space

Dissolved gases in oil are major parameters for assessing the health state of power transformers. Recognizing that the existing state recognition methods are often restrained by data fluctuation and usually require greater computational load, this paper proposes a rapid transformer health state recognition method through Canopy cluster-merging of dissolved gas data in high-dimensional space. Following the introduction of fluctuation coefficient to evaluate the quality of data and the assignation of weight to reflect the difference between gases, the variable-weighted high-dimensional space of dissolved gases is established to suppress the impact from the fluctuated data. The novel Canopy cluster-merging method that overcomes the instability and high computational complexity of the conventional clustering method is then proposed and used in the variable-weighted high-dimensional space to recognize the abnormal state transformer. Applying the state recognition rules and matching with the established abnormal event base, the health state of the transformer could be rapidly recognized. The single case verification concludes that the proposed method has better clustering effect and can significantly improve the clustering speed even by 17.08 times. The group verification test indicates that the proposed method not only demonstrates an accuracy as high as 91.43% but also shows extremely high efficiency compared with the conventional recognition methods.
A New Method to Improve the Transient Performance of Virtual Synchronous Generator with the Extended Virtual Impedance

Although the virtual rotor inertia and damping factor in the virtual synchronous generator (VSG) control can reduce the rate of change of frequency (ROCOF) in the inverter stand-alone mode and contributes to overall frequency stability in the grid-connected mode, they deteriorate transient performance in the grid-connected mode with power/current overshoots and oscillations. In this paper, an extended virtual impedance method is introduced for the first time to improve the transient performance of the VSG active power loop. Compared to the existing solutions, the proposed method achieves more exceptional transient performance in the grid-connected mode with the simpler parameter tuning process. Meanwhile, it also keeps the effective rotor inertia unchanged and maintains the same frequency stability in the grid-connected or stand-alone modes. Finally, the effectiveness of the proposed method is validated and compared with existing method by experimental results.
Context aware service oriented architecture for Web based education

In the past years, most of e-learning researches devoted themselves to the exchange and interoperability of various educational materials such as LOM and SCORM. Recently, IMS Learning Design specification provides a generic and flexible language to support pedagogical diversities in online learning. In this paper, we proposed a context-aware SOA to enable the reusability of both learning designs and learning materials by treating them as Web Services. We utilize OWL-S as the service description language to support reasoning procedures and teachers can utilize the semantic matchmaker to retrieve desired learning designs and learning materials in a semantic manner so that they can compose their learning scenarios more efficiently and flexibly.
Power Network System Identification and Recovery Based on the Matrix Completion

Health detection of power networks is a complex and important issue. In order to reduce the detection complexity of the power network, a matrix recovery method based on the matrix completion has been proposed in this paper. Matrix completion has attracted considerable attentions in computer vision, system identification, and machine learning in recent years. This paper presents a survey on algorithms about matrix completion including Singular Value Threshold (SVT), Alternating Direction Method (ADM) and so on. In order to reproduce algorithms about matrix completion and evaluate the performance of these algorithms, this paper is present. The numerical experiments are conducted to evaluate the performance of algorithms. An application about image denoising is also carried out.
Containerized microservices: Structures and dynamics

Microservice architecture (MSA) is a new kind of service-oriented architecture. The architecture requires that system components are implemented in the form of microservices. Microservices are message-driven services with small size, and they can be independently developed and automatically deployed. Microservices can be built and released in software containers, since a software container can provide an isolated and portable environment for executing programs. This paper observes structures and functions of containerized microservices from a formal point of view. A framework of categories is adopted for modelling structures and dynamics of containerized microservices. Some issues, which include interoperation, registration and accessing of microservices, are discussed in this paper, and relevant models are formed under the help of the category-based tools. The established models show that the adopted framework can be applied in modelling microservice-oriented applications, and it is able to bring functional and structural features of applications closer together.
Title Contributions of Indian Ocean and Monsoon Biases to the Excessive Biennial ENSO in CCSM 3 Permalink

The Community Climate System Model, version 3 (CCSM3), is known to produce many aspects of El Niño–Southern Oscillation (ENSO) realistically, but the simulated ENSO exhibits an overly strong biennial periodicity. Hypotheses on the cause of this excessive biennial tendency have thus far focused primarily on the model’s biases within the tropical Pacific. This study conducts CCSM3 experiments to show that the model’s biases in simulating the Indian Ocean mean sea surface temperatures (SSTs) and the Indian and Australian monsoon variability also contribute to the biennial ENSO tendency. Two CCSM3 simulations are contrasted: a control run that includes global ocean–atmosphere coupling and an experiment in which the air–sea coupling in the tropical Indian Ocean is turned off by replacing simulated SSTs with an observed monthly climatology. The decoupling experiment removes CCSM3’s warm bias in the tropical Indian Ocean and reduces the biennial variability in Indian and Australian monsoons by about 40% and 60%, respectively. The excessive biennial ENSO is found to reduce dramatically by about 75% in the decoupled experiment. It is shown that the biennial monsoon variability in CCSM3 excites an anomalous surface wind pattern in the western Pacific that projects well into the wind pattern associated with the onset phase of the simulated biennial ENSO. Therefore, the biennial monsoon variability is very effective in exciting biennial ENSO variability in CCSM3. The warm SST bias in the tropical Indian Ocean also increases ENSO variability by inducing stronger mean surface easterlies along the equatorial Pacific, which strengthen the Pacific ocean– atmosphere coupling and enhance the ENSO intensity.
A Travel Route Recommendation System Based on Smart Phones and IoT Environment

Tourism recommendation systems play a vital role in providing useful travel information to tourists. However, existing systems rarely aim at recommending tangible itineraries for tourists within a specific POI due to their lack of onsite travel behavioral data and related route mining algorithms. To this end, a novel travel route recommendation system is proposed, which collects tourist onsite travel behavior data automatically regarding a specific POI based on smart phone and IoT technology. Then, the proposed system preprocesses the behavior data to transform raw behavior sequences into Tourist-Behavior pattern sequences. Subsequently, the system discovers frequent travel routes from the generated pattern sequences by using an original route mining algorithm, named Tourist-Behavior PrefixSpan. Finally, a route-recommending method is designed to search and rank tangible travel routes according to the querying tourist’s profile and constraint. The experimental results demonstrate that the proposed system is efficient and effective in recommending POI-oriented tangible travel routes considering tourists’ route constraints and personal profile while ensuring that the suggested routes have considerable route values.
RSSI-Based Localization Using LoRaWAN Technology

The Internet of Things (IoT) is increasing in size by having more devices connected to it as they are becoming low-cost to manufacture and easier to connect to the internet. New use cases are being created by the need for it and feasibility to provide it, with low-cost solutions. As a key enabler of the IoT, Long Range Wide Area Network (LoRaWAN) is gaining great attention in research and industry. It provides a desirable solution for applications that require hundreds or thousands of actively connected devices to monitor a process or an environment or to assist in controlling a certain process. Some of these IoT use cases require having the location information of the IoT devices. In some cases, localization can be the intrinsic purpose of deployment. In this regard, the received signal strength indicator (RSSI)-based localization offers a feasible and affordable solution. Since LoRaWAN has only been there for only a few years, research on utilizing LoRaWAN RSSI for localization purposes is in early stages and is scarce. In this paper, we study LoRaWAN RSSI-based localization and evaluate its accuracy, impairments, and prospects. In addition, we employ the use of software-defined radios (SDR) into our work for the purpose of path-loss characterization. The experimental results revealed the fact that a high variance of RSSI due to frequency hopping feature of LoRaWAN could severely impact the localization performance. Potential solutions are developed and presented to reduce this negative impact, hence improve the performance.
Workload Scheduling in Vehicular Networks With Edge Cloud Capabilities

In order to support the development of 5G technologies, researchers are actively engaged in addressing the challenges accompanying the emerging 5G applications. Unquestionably, an eminent technology gaining significant research attention is edge computing. Vehicular edge computing brings data storage and computing capabilities as well as hosting support applications that comprise emerging vehicular services and applications which demand low-delay processing, to the edge closer to the vehicles, reducing response time and increasing reliability, therefore achieving the holistic vision of the tactile Internet. In this context, this paper considers a vehicular network with edge computing capabilities deployed at road side units, and addresses the problem of workload offloading as well as scheduling of computation tasks on the computing resources available at the edge. The challenge here is the high mobility of the vehicles and hence their short residence time within the coverage range of the road side units hosting the edge computing resources. A joint problem considering the communication and computation resources, as well as the latency requirements of the workload is formulated and the scheduling is shown to be NP-Hard. Subsequently, efficient solutions based on Lagrangian relaxation are derived and presented. We evaluate numerically the proposed methods and show their closeness to the optimal solutions.
Structure of eigenvectors of random regular digraphs

Let $n$ be a large integer, let $d$ satisfy $C\leq d\leq \exp(c\sqrt{\ln n})$ for some universal constants $c, C>0$, and let $z\in {\mathcal C}$. Further, denote by $M$ the adjacency matrix of a random $d$-regular directed graph on $n$ vertices. In this paper, we study structure of the kernel of submatrices of $M-z\,{\rm Id}$, formed by removing a subset of rows. We show that with large probability the kernel consists of two non-intersecting types of vectors, which we call very steep and sloping with many levels. As a corollary of this theorem, we show that every eigenvector of $M$, except for constant multiples of $(1,1,\dots,1)$, possesses a weak delocalization property: all subsets of coordinates with the same value have cardinality less than $Cn\ln^2 d/\ln n$. For a large constant $d$ this provides a principally new structural information on eigenvectors, implying that the number of their level sets grows to infinity with $n$, and -- combined with additional arguments -- that the rank of the adjacency matrix $M$ is at least $n-1$, with probability going to 1 as $n\to\infty$. Moreover, our main result for $d$ slowly growing to infinity with $n$ contributes a crucial step towards establishing the circular law for the empirical spectral distribution of sparse random $d$-regular directed graphs. As a key technical device we introduce a decomposition of ${\mathcal C}^n$ into vectors of different degree of "structuredness", which is an alternative to the decomposition based on the least common denominator in the regime when the underlying random matrix is very sparse.
VHDL modelling of the wireless audio transceiver through IEEE802.3 and IEEE802.11

: The wireless communication is highly deployed due to it convenience of mobility. The wireless local area network, WLAN is dominated by IEEE802.11 standard. Wired local area network (LAN) is the network backbone of the WLAN. Almost all the new desktops’ motherboard are equipped with the Ethernet connecter and all new notebooks are equipped will IEEE802.11 WLAN. This evoked us to develop a wireless audio transceiver that compatible with IEEE802.3 and IEEE802.11 protocols. The VHDL modelling is done to model transceiver that convert the audio signal to IEEE802.3 packets to be transmitted through IEEE802.3 and IEEE802.11. The VHDL is selected to model the transceiver and VHDL is defined in IEEE as a tool of creation of electronics system because it supports the development, verification, synthesis and testing of hardware design, the communication of hardware design data and the maintenance, modification and procurement of hardware [1]. The transceiver consists of two individual modules, the transmitter and the receiver. The original audio signal is segmented to UDP packets that compatible with the IEEE802.3 protocol. The audio packets are broadcasted out through wireless access point (AP). After the audio packets received by the wireless clients through IEEE802.11 and IEEE802.3, the receiver will convert the audio packets back to audio signal. By broadcasting the data packets, all receiving speakers will be able to receive the data packets and convert the digital bits into audio wave. This gives the user the flexibility to place as many speakers as they want. The Cyclic Redundancy Check algorithm is not included in the transceiver because audio steaming is real time application, there is always not enough time to retransmit the audio packets that are corrupted. The proposed transceiver achieves 12kbps and sufficient to support audio streaming
Source Broadcasting and Asymmetric Data Transmission With Bandwidth Expansion

We consider the broadcasting of a scalar memoryless Gaussian source over a two-user Gaussian broadcast channel with bandwidth expansion. In concurrent with the source description, a message that should be decoded reliably just by the “higher quality” user is sent. Conditioned on the message rate, we derive a necessary condition for the achievability of a mean-squared-error distortion pair at the two receivers, which extends the Reznic–Feder–Zamir bound. This necessary condition and a sufficient condition that we derive establish the set of achievable energy-distortion exponents for this setting.
Data Analytics: Why Data Normalization

The two maestros Artificial Intelligence and Machine learning are ruling the data filled world with good analytics. Many of these domain skills are used in the industry to analyze and interpret the data beyond what it actually is. Supporting the known saying find the horse before the cart is ready is what it mean to normalize the data before getting it analyzed. This article focus on what normalization actually is, why normalization is needed before data analysis and how data normalization is done.
DeepSCC: Deep Learning-Based Fast Prediction Network for Screen Content Coding

Screen content coding (SCC) is an extension of high efficiency video coding (HEVC), and it is developed to improve the coding efficiency of screen content videos by adopting two new coding modes: Intra Block Copy (IBC) and Palette (PLT). However, the flexible quadtree-based coding tree unit (CTU) partitioning structure and various mode candidates make the fast algorithms of the SCC extremely challenging. To efficiently reduce the computational complexity of SCC, we propose a deep learning-based fast prediction network DeepSCC that contains two parts: DeepSCC-I and DeepSCC-II. Before feeding to DeepSCC, incoming coding units (CUs) are divided into two categories: dynamic CTUs and stationary CTUs. For dynamic CTUs having different content as their collocated CTUs, DeepSCC-I takes raw sample values as the input to make fast predictions. For stationary CTUs having the same content as their collocated CTUs, DeepSCC-II additionally utilizes the optimal mode maps of the stationary CTU to further reduce the computational complexity. Compared with the HEVC-SCC reference software SCM-8.3, the proposed DeepSCC reduces the encoding time by 48.81% on average with a negligible Bjøntegaard delta bitrate increase of 1.18% under all-intra configuration.
Three - dimensional garment virtual fitting method with local area geometric feature enhancement based on Voronoi diagram

In order to realize the accurate matching of 3D garment model and human model, a new method for feature extraction of garment and human model is proposed. We first construct local mesh fitting operator based on Voronoi graph and weighted parametric surface. Secondly, on the basis of the use of geometric attributes, the change in the neighborhood is used as the reference value. On this basis, the region is divided and then the feature point extraction of costume model and human body model is realized. Thirdly, a robust feature matching function is established. Finally, the clothing and the human body model can be matched quickly and accurately.
Helical Force Effects on the Onset of Küppers-Lortz Instability with Free-Free Boundaries

The rotating Rayleigh-Benard convection in the presence of helical force is modeled by a four mode Lorenz model. This model is extended to study how this force affects the onset of Kuppers-Lortz (KL) instability. We observed that when S < 3ߨ , the critical Taylor number and the critical angle for the onset of KL instability decrease as helical force intensity S increases. This influence of helical force is similar to that obtained in rotating fluid layer under periodic modulation of the rotation rate (Bhattacharjee, 1990). In the range 3 ൑ S ൑14.9246, the system exhibits the reentrant behavior of rolls demonstrating the constructive and destructive role of rotation in the KL instability apparition. In this case, we observed that the application of this force allows the KL instability for small values of Taylor number. In addition, it has been found that there exists a threshold (14.9246) in the magnitude of the helical force that allows suppressing the KL instability in the system for any value of Taylor number.
A Novel Structural Decomposition Method for Error Propagation Mechanism Analysis Based on Meta-Action Unit Theory

A reasonable structural decomposition method is the foundation of studying the error propagation mechanism precisely. In this paper, a new Function-Motion-Action-Part (FMAP) structural decomposition method is proposed to analyze the mapping model between the geometric dimension of part and output errors of a mechanical product. First, the meta-action unit (MAU) theory is extended to the part layer, and the MAU structure and the MAU chain are redefined. Second, according to the coordination characteristics of the parts in MAU, the error expression model of the parts and the error propagation model of the MAU assembly process are established. The state-space model of the MAU output error is further established so as to complete the mapping model from the geometric dimension of manufactured parts to the precision of the MAU. Then, according to the characteristics of the kinematic error propagation between the MAUs, the input and output error indexes of the MAU chain are determined, and the error propagation model in the MAU chain is established based on the radial basis function network. Finally, the methodology is applied to a numerical control rotary table to verify the effectiveness of the FMAP structural decomposition method for the error propagation mechanism analysis.