Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Analysis of Technologies for Audio Data Transmission

There are some limitations using wifi, bluetooth or QR code when we communicate two devices on medical, rural or factories environment. In contrast, the communication via sound has interesting advantages over these technologies. In this paper we propose a comparative analysis of technologies for Audio Data Transmission (ADT). We analyze different ADT technologies that we selected validating some features such as the date of the last update or the support for Android operative systems. Subsequently, we tested two Android mobile applications using ADT technologies: Quiet and SinVoice. Also, we used two profiles for each technology: audible and inaudible. The applications send and receive a string as a byte array, and also they capture the delivery time in milliseconds. Then, we evaluated the results obtained through a comparative analysis in terms of distance, quantity of transmitted bytes and the time of sending and receiving of the information. The final results show the delivery time measurements of the technologies for each proposed scenario summarizing in tables and graphs.
EDLT: Enabling Deep Learning for Generic Data Classification

This paper proposes to enable deep learning for generic machine learning tasks. Our goal is to allow deep learning to be applied to data which are already represented in instancefeature tabular format for a better classification accuracy. Because deep learning relies on spatial/temporal correlation to learn new feature representation, our theme is to convert each instance of the original dataset into a synthetic matrix format to take the full advantage of the feature learning power of deep learning methods. To maximize the correlation of the matrix, we use 0/1 optimization to reorder features such that the ones with strong correlations are adjacent to each other. By using a two dimensional feature reordering, we are able to create a synthetic matrix, as an image, to represent each instance. Because the synthetic image preserves the original feature values and data correlation, existing deep learning algorithms, such as convolutional neural networks (CNN), can be applied to learn effective features for classification. Our experiments on 20 generic datasets, using CNN as the deep learning classifier, confirm that enabling deep learning to generic datasets has clear performance gain, compared to generic machine learning methods. In addition, the proposed method consistently outperforms simple baselines of using CNN for generic dataset. As a result, our research allows deep learning to be broadly applied to generic datasets for learning and classification (Algorithm source code is available at http://github.com/hhmzwc/EDLT).
Fault Modeling and Simulation Based on Cyber Physical System in Complex Distribution Network

With the high penetration of renewable energy in distribution network and the gradual improvement of smart grid construction, the physical processing and information processing are closely coupled. Complex distribution network is a typical cyber physical system(CPS). The sequential property of electric power system and the time-discrete property of communication system have made the research of CPS for distribution network complicated. In view of dynamic process of CPS for distribution network, the fusion modeling and large-scale simulation calculation and platform of CPS for distribution network were investigated. First, the characteristics and the interaction principle of CPS for distribution network were analyzed. And the coupling mode of CPS for distribution network was proposed. Next, the simulation model of physical system was proposed. From different information data types, information data flow was modeled. On this basis, a framework for integration of multiple cyber physical modules was constructed. And CPS fusion method was proposed. Taking the simulation platform of CPS for distribution network as example, characteristics of RT-LAB as the physical simulation tool, and OPNET as the information simulation tool were analyzed. And the feasibility of co-simulation of RT-LAB and OPNET was elaborated. Finally, a fast simulation framework based on RT-LAB was established. And a cooperative simulation for electrical power system and control system was proposed. Based on the essential difference between physical events and information events, two kinds of simulation timing synchronization methods suitable for physical model and information model were presented. A 62-nodes distribution network with distributed generators of an area was modeled on RT-LAB. The simulation results of CPS platform based on RT-LAB and OPNET show that when a single-phase fault occurs in a complex distribution network, communication problems such as congestion delays and error codes of information system in the transmission of measurement data and control instruction cause failures to be dealt with in time delay to trigger a cascading fault. The results effectively verified the importance of control information system to monitor distribution network. At the same time, the CPS simulation platform for the actual environment of complex distribution network is necessary.
Waijung Blockset-STM32F4 Environment for Real Time Induction Motor Speed Control

Induction motors of wound rotor or squirrel cage type are widely used in industry application due to their low cost and robustness. Nevertheless, for a long time, these types of motors were used especially in variable speed drives. During the two last decades, due to the great progress in microelectronic control systems and power converters they became also an important in controlled constant speed drives. This paper presents a solution to control an AC induction motor (IM) using Waijung Blockset and STM32F4 DISCOVRY that enable cost-effective design, reducing the system components and increase efficiency. The aim of this work is to keep a constant speed at variable mechanical torque. Thus a closed loop speed control for an induction motor with scalar control technique is presented. Verification of the proposed controller is provided by experimental tests. The experimental results show the effectiveness of the speed controller with a wide range of load.
A Z Structure Convolutional Neural Network Implemented by FPGA in Deep Learning

In this paper, a FPGA implementation method for convolutional neural networks of deep learning is proposed. In order to solve the problem of efficiency and speed of depth learning algorithms, a Z convolutional neural network structure is presented. The structure is easy to implement with FPGA. At the same time, in order to meet the requirement of complex multi-input and multi-output practical application system and increase the speed of calculations, a 3 dimensional Z type convolutional neural network structure is proposed, and the function of hardware circuit is verified by circuit simulation.
Coordination on Industrial Load Control and Climate Control in Manufacturing Industry Under TOU Prices

Demand side management (DSM) can help promote the efficiency and reliability of a power system. However, the benefits of DSM in the manufacturing industry will be weakened if the interactions between industrial load control and climate control are not considered. For example, industrial load control may impact the indoor temperature and humidity, affecting climate control. This interaction may ultimately result in an unexpected deviation from the ideal DSM plan. Hence in this paper, coordinated industrial load control and climate control are investigated in a cold machine shell manufacturer under time-of-use prices. The interaction of industrial loads and climate control is modeled, and a model predictive control-based method is proposed to address forecast uncertainties. The objective of DSM is to minimize electricity costs meanwhile meeting the respective objectives of the industrial load control and climate control. Case studies have verified the above benefits for a cold machine shell manufacturer, and the results show that additional reductions in demand during peak hours can be achieved using the proposed method.
CADEN: A Context-Aware Deep Embedding Network for Financial Opinions Mining

Following the recent advances of artificial intelligence, financial text mining has gained new potential to benefit theoretical research with practice impacts. An essential research question for financial text mining is how to accurately identify the actual financial opinions (e.g., bullish or bearish) behind words in plain text. Traditional methods mainly consider this task as a text classification problem with solutions based on machine learning algorithms. However, most of them rely heavily on the hand-crafted features extracted from the text. Indeed, a critical issue along this line is that the latent global and local contexts of the financial opinions usually cannot be fully captured. To this end, we propose a context-aware deep embedding network for financial text mining, named CADEN, by jointly encoding the global and local contextual information. Especially, we capture and include an attitude-aware user embedding to enhance the performance of our model. We validate our method with extensive experiments based on a real-world dataset and several state-of-the-art baselines for investor sentiment recognition. Our results show a consistently superior performance of our approach for identifying the financial opinions from texts of different formats.
A Review of Wind Turbine Deloaded Operation Techniques for Primary Frequency Control in Power System

Wind farm is rapidly increasing across the world as a cheaper source of green energy and as fossil fuel is depleting every day. Most conventional power plants have been replaced with wind turbines generators as a result of the high increasing penetration of renewable energy especially wind power, but this has negatively impacted on the frequency stability and reliability of power systems due to wind speed variability. A required frequency can't be compromised, so a new global grid code regulation mandates all wind turbine generators (WTGs) in an interconnected electricity network to participate in primary frequency control (PFC) for the curtailment of frequency disturbance in the power system as a result of generation and load consumption imbalance. Literature shows that the deloaded mode of operation which makes the wind turbine participate in the primary frequency control is still emerging and very vast in scope and has a critical role to play as reliability and availability of electric power in this industrial age is concern. This paper provide an extensive up-to-date literature review of deloaded mode of operation for primary frequency regulation of wind turbine generators in an interconnected power system. Various control modes and comparisons with the deloaded mode are highlighted as well. Again, an assessment into some factors that determine quantity and quality of the power reserve margins, the percentage of reserve margin per the level of wind farm penetration and some optimization processes as well as projected future research works will be outlined.
Detecting and Modelling Air Flow Overprovisioning / Underprovisioning in Air-Cooled Datacenters

When cooling and exhaust air flows in air-cooled datacenters mix, the energetic efficiency of the cooling operations drops. One way to prevent this mixing of happening is by augmenting the air tightness of the hot and cold aisles; this, however, requires installing opportune hardware that may be expensive and require time consuming installations. Alternatively, one may try to minimize cooling and exhaust air flows mixing by opportunely controlling the speeds of the fans of the Computer Room Air Handling (CRAH) units so that the distribution of the air pressure field within the computer room is favorable. Implementing this type of flow control requires both detecting when there actually is some type of flow mixing somewhere, plus understanding how to operate the cooling infrastructure so that these mixings do not happen. To this aim, there is the need for models that can both help deciding whether these mixing events occur, plus designing automatic control strategies for reducing the risks that they will happen. In this manuscript, we propose an ad-hoc methodology for the data-driven derivation of control-oriented models that serve the purposes above. The methodology is built on classical Prediction Error Method (PEM) approaches to the system identification problem, and on laddering on the peculiarities of the physics of the phenomena under consideration. Moreover, we test and assess the methodology on a industrial-scale air-cooled datacenter with an installed capacity of 240 kW, and verify that the obtained models are able to capture the dynamics of the system in all its potential regimes.
Data-Driven Approach to Support Experts in the Identification of Operational States in Industrial Process Plants

Knowledge on the operating conditions of chemical processes is important for various applications, for instance failure diagnosis. In absence of the recorded information, it has to be reconstructed from other data. This is commonly carried out by process experts as the identification of the operating states requires a deep understanding of the underlying process. As manual classification is a time-consuming task, an automatic identification can simplify this task greatly. As data-driven approaches often fail due to the complex nature of the processes, two hybrid approaches are proposed in this paper, supporting the expert during the classification. Big data methods are used for processing with experts having the chance to influence and evaluate the algorithms and results. For identification, a k-Means clustering and a combination of self-organizing maps and hidden Markov models are used. While minimizing the expert effort for classification, the results still have to be reliable. Both approaches performed accurate and decreased the expert effort significantly. Future studies are centered on combining both methods as their strengths complement each other.
Analysis of a Symmetrical Nine-phase Machine with Highly Non-Sinusoidal Back-Electromotive Force

In this paper a two-pole surface permanent magnet synchronous machine (PMSM) with a symmetrical nine-phase winding configuration is investigated. Magnets on rotor are shortened which causes production of highly non-sinusoidal back-electromotive force (EMF) in the stator windings. Analysis of the EMF reveals a third-harmonic component, which is almost equal in magnitude to the fundamental. These harmonics have been used by researchers in the past to increase the torque density of multiphase machines. In this paper, a specific rotor structure is designed and tested under third-harmonic current injection in order to analyse torque improvement. It is shown that optimal injection ratio and torque improvement are dependent on the ratio of third-harmonic back-EMF to the fundamental one. The so-called ‘same RMS value’ constraint method is used for calculation of the optimal level of the third-harmonic current injection. It is found that the torque can be increased by up to 36% for the studied machine. Obtained torque increase results are validated using finite element method (FEM) simulations. Finally, the results acquired with the described rotor structure are compared with those obtained for rotor with full magnets and near-sinusoidal back-EMF. It is shown that for approximately 4 times less magnet material in non-sinusoidal configuration and with third-harmonic current injection, the total electromagnetic torque is only 22% lower.
DC-Series PV Collection DC/DC Converter with Wide Output Voltage Regulation Range

DC-series photovoltaic (PV) Plant has many advantages. As the most important device in the system, DC/DC converter has some special technical requirements compared to other applications, especially in the aspect of that the output voltage of the DC-series converter has wider regulation range. The paper proposes an IPOS cascaded converter, which is based on Boost Full Bridge Isolated DC/DC Converter (BFBIC). As a basic module, cascaded through input parallel output series (IPOS) method to improve power capacity and output voltage. Besides some normal operating modules and several backup modules are set in the converter to improve output voltage adjustment range and to meet with the needs of the DC-series PV generation units. A 20kV/500kW DC/DC converter model and 60kV/1.5MW system model are built in MATLAB/SIMULINK to verify the proposed topology and control strategies.
Personalized Intelligent Push Method of Design Tasks for Designers in the Hackerspace

In order to improve the efficiency and quality of the design tasks of Hackerspace and reduce the waste of designer resources, the personalized intelligent push method of design tasks for designers in the Hackerspace is proposed. Extracting the relevant information of designers in the database of traditional Hackerspace platform, the personalized feature model of designers is generated based on the KSAO theory and updated synchronously. Being intelligently analyzed by the Hackerspace platform, the design task demand vector is generated and then the design task demand vector set will be formed. With the adoption of the method proposed in this paper, the matching degree between designers and design tasks can be calculated, so as to implement the push of specific design tasks to particular designers. Taking the creative design of injection molding machine parts as an example, the feasibility of this method in engineering application is verified.
Levels of Spatial and Temporary Non-Ionizing Radiation produced by Electromagnetic Fields from Telecommunications Services

A computer model that promotes determining the Specific Absorption Rate (SAR) will be develop under the condition of temporary exposure of people to electromagnetic radiations which are non-ionizing according to the models of Virtual Family v2.0. In order to do this, measurements of electric field intensity produced by radio, television and mobile phones have been considered for a seven day continuous period in a single location to study the temporary exposure, and ten different places within the city of Riobamba for the spatial measurements with the models Duke, Ella, Billie and Thelonious. This showed the spatial exposure of the models to these radiations. When comparing the measured values to the limits fixed by the International Commission on Non-Ionizing Radiation Protection (ICNIRP), It can be assured that the radiation levels are below the maximum established by the norms. Neither spatial nor temporary radiation levels have a considerable variation if the intensity level of the electric field is considered between 0.1 V/m and 3 V/m. The behavior is similar allowing for the absorption of the mentioned fields. Once the variation range of the electric field was obtained, the error level produced by the computer model concerning SAR could be determined. It is highly recommended for studies of non-ionizing radiation safety; for instance, it is recommended for epidemiological.
A 0.5-5.6 GHz Inductorless Wideband LNA with Local Active Feedback

In this paper, a wideband low noise amplifier (LNA) with local active feedback is proposed. The proposed LNA exploits a differential common-source stage with current-reuse technique to reduce the power consumption. A source follower is utilized as an active feedback path and the feedback signal is taken preceding the cascode transistor to broaden the bandwidth and improve the linearity. Implemented in a 130-nm SOI CMOS technology, the proposed LNA achieves simulated 21.1 dB voltage gain with a 3-dB bandwidth of 0.5-5.6 GHz without using any inductors. The minimum NF is 3.63 dB while the maximum IIP3 is −4.86 dBm. The power consumption is 15.2 mW under a supply voltage of 1.5 V.
High-dimensional linear operation for phase-coherent spatial modes

High-dimensional linear operations are significant for the quantum optics. A non-cascaded approach is demonstrated to achieve arbitrary linear operations for phase-coherent spatial modes. With spatial light modulators (SLMs), unitary transformation matrices are obtained with dimensionalities ranging from 7 to 24 with the fidelities from 95.1% to 82.1%, respectively. Non-unitary matrix transformations are also presented in this work. Much higher dimensionality has been achieved compared to the previous reports with our approach, which is very promising for various applications in quantum domain.
All-Fiber Orbital Angular Momentum (OAM) Broadband Functional Devices for OAM Generation and Beam Splitting in Conventional Graded-index Multimode Fiber

We propose, design and fabricate all-fiber broadband orbital angular momentum (OAM) generator and OAM 1×2 coupler in conventional graded-index multimode fiber, based on which OAM±1 and OAM±2 modes with high purity from 1480 nm to 1620nm are generated and OAM±1 modes are split into two branches over the C-band.
k-Optimized Path Routing for High-Throughput Data Center Networks

Network throughput has become an important issue for big-data analysis on Warehouse-Scale Computing (WSC) systems. It has been reported that randomly-connected inter-switch networks can enlarge the network throughput. For irregular networks, a multi-path routing method called k-shortest path routing is conventionally utilized. However, it cannot efficiently exploit longer-than-shortest paths that would be detour paths to avoid bottlenecks. In this work, a novel routing method called k-optimized path routing to achieve high throughput is proposed for irregular networks. We introduce a heuristic to select detour paths that can avoid bottlenecks in the network to improve the network throughput. Experimental results show that the proposed k-optimized path routing can improve the throughput by up to 133 % compared to the conventional k-shortest path routing. Moreover, it can improve the network bandwidth while maintaining the low network latency.
Predicting Protein Functions of Bacteria Genomes via Multi-instance Multi-Label Active Learning

Bacteria are workhorses in the fields of molecular biology, genetics, and biochemistry. Understanding the biological functions of proteins is important for bacteria studies in the post-genomic era. There are a large number of proteins with unknown biological functions in newly discovered bacteria species. Active learning can assist biologists in selecting the most valuable proteins as candidates for assays. Previously, we formulated the protein function prediction problem as a multi-instance multi-label (MIML) learning task. Here, we propose a MIML active learning algorithm MIMLAL-R for protein function annotation of bacteria genomes. MIMLAL-R minimizes an approximated surrogate loss by stochastic gradient descent with the most valuable bag-label pairs, which are chosen by a selection criterion that combines label cardinality inconsistency and diversity. We tested MIMLAL-R on four real bacteria, namely Geobacter sulfurreducens, Azotobacter vinelandii, Shewanella loihica PV-4, and Escherichia coli strain K12. The MIMLAL-R approach achieved excellent performances in all cases. The codes and data can be freely downloaded from.
Multi-Port Power Conversion Systems for the More Electric Aircraft

In the framework of the More Electric Aircraft (MEA), weight reduction and energy efficiency constitute the key figures. In addition to these requirements, the safety and the continuity of operation is of critical importance. These sets of desired feature are in disagreement, because the redundancy needed to guarantee the safety of operation implies necessarily in additional weight. Several concepts of the electrical power distribution systems have been proposed, in this manuscript, an approach based on the widespread use of multi-port power converters both for the DC/DC and for the DC/ AC stages is proposed. These two technologies allows for a ring distribution of the electrical power whereas the galvanic isolation is still kept. Simulation and experimental results show the feasibility of the concept in common and fault operation conditions.