Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Battery Energy Storage System on Frequency of Islanded Microgrid

Microgrid has become an important concept in global power system in recent years. A large amount of generated power is lost in transmission due to lack of proper utilization. Microgrid helps to reduce power losses and it makes the power system stable and reliable. Frequency control is very essential phenomenon for maintaining the stability of power system. A small frequency problem can create severe effect in whole power system like blackouts. From simulated results, the impact of battery energy storage system on frequency of islanded microgrid can be discussed.
GAN-based Augmentation for Populating Speech Dataset with High Fidelity Synthesized Audio

In this paper, we present an audio augmentation method that generates synthetic audio using Generative Adversarial Networks (GANs). We propose a training strategy that first uses Harmonic Percussive Source Separation (HPSS) to extract spectral features and then improves the fidelity of the synthesized audio by applying progressively-growing GANs. Our method is demonstrated on a public speech dataset released by Google TensorFlow. When employing our method, the performance evaluated by Frèchet Inception Distance (FID) showed 12.514, but 14.012 as for the existing image-generating GANs (lower FID indicates better fidelity).
A Trust Management System for the IoT domain

In modern internet-scale computing, interaction between a large number of parties that are not known a-priori is predominant, with each party functioning both as a provider and consumer of services and information. In such an environment, traditional access control mechanisms face considerable limitations, since granting appropriate authorizations to each distinct party is infeasible both due to the high number of grantees and the dynamic nature of interactions. Trust management has emerged as a solution to this issue, offering aids towards the automated verification of actions against security policies. In this paper, we present a trust- and risk-based approach to security, which considers status, behavior and associated risk aspects in the trust computation process, while additionally it captures user-to-user trust relationships which are propagated to the device level, through user-to-device ownership links.
On the Effect of Shadowing Correlation on Hybrid Precoding Performance for Cell-free mmWave massive MIMO UDN System

One of promising transmission technologies for beyond-5G wireless communication is cell-free massive MIMO where all the access nodes (ANs) cooperate to coherently serve all the user equipments (UEs). Based on the benefits from massive MIMO, the novel notion of cell-free network architecture is adequate to overcome the inter-cell interference limitation in ultra-dense network (UDN) environments, owing to the full cooperation among ANs. In this paper, we develop synergetic operations between the cell-free massive MIMO and mmWave ultra-wide frequency band, which is able to provide a genuine high-quality and robust wireless pervasive service by fully utilizing the basic three pillars for wireless capacity, such as bandwidth, spectral efficiency and network densification. For this goal, we study the impacts of analog precoding schemes to be combined with zero-forcing digital precoding on cell-free mmWave massive MIMO UDN systems. In addition, we investigate the effect of correlated shadowing on the average spectral efficiency performance for the proposed hybrid precoding schemes in both outdoor and indoor UDN environments.
Assessment of the Relative Importance of different hyper-parameters of LSTM for an IDS

Recurrent deep learning language models like the LSTM are often used to provide advanced cyber-defense for high-value assets. The underlying assumption for using LSTM networks for malware-detection is that the op-code sequence of a malware could be treated as a (spoken) language representation. There are differences between any spoken-language (sequence of words/sentences) and the machine-language (sequence of op-codes). In this paper we demonstrate that due to these inherent differences, an LSTM model with its default configuration as tuned for a spoken-language, may not work well to detect malware (using its op-code sequence) unless the network’s essential hyper-parameters are tuned appropriately. In the process, we also determine the relative importance of all the different hyper-parameters of an LSTM network as applied to malware detection using their op-code sequence representations. We experimented with different configurations of LSTM networks, and altered hyper-parameters like the embedding-size, number of hidden-layers, number of LSTM-units in a hidden layers, pruning/padding-length of the input-vector, activation-function, and batch-size. We discovered that owing to the enhanced complexity of the malware/machine-language, the performance of an LSTM network configured for an Intrusion Detection System, is very sensitive towards the number-of-hidden-layers, input sequence-length and the choice of the activation-function. Also, for (spoken) language-modeling, the recurrent architectures by-far outperforms their non-recurrent counterparts. Therefore, we also assess how sequential DL architectures like the LSTM compares against their non-sequential counterparts like the MLP-DNN for the purpose of malware-detection.
Indian Stock Market Prediction using Deep Learning

In this paper, we predict the stock prices of five companies listed on India’s National Stock Exchange (NSE) using two models- the Long Short Term Memory (LSTM) model and the Generative Adversarial Network (GAN) model with LSTM as the generator and a simple dense neural network as the discriminant. Both models take the online published historical stock-price data as input and produce the prediction of the closing price for the next trading day. To emulate the thought process of a real trader, our implementation applies the technique of rolling segmentation for the partition of training and testing dataset to examine the effect of different interval partitions on the prediction performance.
Fluorescent detection of NO2 by free base tetraphenyl porphyrins

The absorption and fluorescence of free base, tetraphenyl-substituted porphyrins are shown to be sensitive to exposure to NO2. The Soret band absorption red-shifts and fluorescence is quenched by an order of magnitude. The fluorescence spectrum of a gassed sample is also red-shifted and broadened following exposure. Several solid state embodiments of a fluorescent sensor based upon free base porphyrins are proposed.
Fault Trending of an Automatic Double Threaded Trampoline Webbing Machine

This paper applies signal processing methodology on a specially designed webbing machine to evaluate and improve the trampoline manufacturing process. Digital Signal Processing (DSP) methods such as Fast Fourier Transform (FFT), and Statistical Process Control (SPC) have been applied to find singular frequencies, but the results are not satisfactory. Instead, a Continuous Wavelet Transform (CWT) based methodology is used to predict the fault before it occurred. Unlike the traditional wavelet scalogram method, the collected data in the research is separated into small segments, and the divided segments are used to generate scalograms. By observing and counting the pulses in different frequency ranges in scalograms, the singular frequency ranges can highlight the trending to fault occurrence. Additionally, a timer is used in the experiment to record the time when the fault occurred. This information validates accuracy of the method and demonstrates that this novel fault trending detection method has a promising usage in recognizing the trending of faults.
Design and Development of an Anthropomorphic Robotic Hand Using TPU Material

The paper presented here approaches to solve the complexity of an anthropomorphic robotic hand. Robotics is now the most advancing field and there has always been an intention of mimicking human-like actuation and behavior. An anthropomorphic hand is one of the approaches to imitate human-like operations. In this paper, the idea of developing an anthropomorphic hand with 15 degrees of freedom and 5 actuators has been elaborated as well as the mechanical design, control system, composition, and peculiarities of the robotic hand have been discussed. The hand has an anthropomorphic appearance and also can perform human-like functionalities, for example, gripping and hand gestures representation. The results reveal that the hand is designed as one part and does not need any kind of assembly and it exhibits an excellent weight lifting capacity, since it is made of flexible thermoplastic polyurethane (TPU) material, and its elasticity also ensures that the hand is safe for interacting with humans as well. This hand may be used in a humanoid robot as well as a prosthetic hand. The limited number of actuators makes the control simpler and the hand lighter.
A Study on Testing Autonomous Driving Systems

In recent years, with the rapid development of artificial intelligence and other related technologies, the traditional automotive industry has begun to integrate information technology in an all-round way. Due to the contributions of computer vision, deep learning, and sensitive sensors, autonomous driving systems (ADS) has now achieved great progress. But as we all know, the primary requirement for autonomous driving is absolute safety. However, technology innovation has brought great challenges to the testing of ADS, and due to the high cost of field testing, industrial companies rarely open relevant test data for research. This paper aims to study existing testing methods for ADS. Our study shows that there are few published works focusing on testing aspects of ADS. However, there is an obvious trend on the record of published works on testing ADS. Also, we can find that most reviewed works focus on setting up virtual test environment including generating, synthesizing, or reconstructing test input data. They either treat ADS as a whole to conduct (sub) system level testing or limit ADS into certain scenarios. From this, we believe that testing of ADS has just begun to attract researchers' interest; great effort should be paid before ADS becomes maturer.
Creative Computing based Experimental Study of Somatosensory Games for Promoting Intention Understanding

This paper attempts to understand the theoretical connotation of creative computing constructively through an experimental study. In this experiment, the somatosensory game designed and developed by integrating multidisciplinary knowledge and human power can help leaners develop their intention understanding ability. The data showed that the leaners in the experimental group performed better in intention understanding tasks in the somatosensory interaction environment. The activation of motor related brain regions in mirror neurons may be the basis for understanding the experimental results. From the design and conclusion of this experiment, we constructively verify that a creative computing based method is an effective one.
Ridesharing based on a Discrete Self-adaptive Differential Evolution Algorithm

Ridesharing provides an effective approach to reduce the number of cars, fuel consumption and greenhouse gas emissions in the environment. The problem to match passengers and drivers according to their requirements is called a ridesharing problem. Recently, many algorithms have been proposed to solve the ridesharing problem. For example, several meta-heuristic algorithms based on Differential Evolution (DE) approach have been proposed to solve the ridesharing problem. In this paper, we will propose a discrete self-adaptive Differential Evolution algorithm (SaNSDE) with neighborhood search to solve the ridesharing problem. In addition, we will compare with two variants of DE approaches to the ridesharing problem to illustrate effectiveness of the proposed SaNSDE algorithm. Several experiments have been conducted to compare the performance of SaNSDE and two variants of DE algorithms. The results indicate that the proposed SaNSDE algorithm outperforms the two variants of DE algorithms in the literature.
Erosion rate and voltage distribution in contracted (with cathode spot) and diffuse (spotless) low-current vacuum arcs

The influence of ambient gas pressure and emission current on the potential distribution in a diffuse arc has been studied. The conditions determining the formation of the potential 'hump' near the cathode have been determined. The erosion rate Upsilon approximately=10-3 g C-1 for an individual crater (local erosion rate) has been measured for a contracted vacuum arc. In both types of arc the local erosion rate is well in excess of the erosion rate measured by the weighing method. The conclusion is drawn that most of the evaporated atoms ( approximately= 90%) have to return to the cathode. The diffuse and the contracted vacuum arcs reveal common properties despite a great difference in the current density. The idea of the potential hump can unite both types of arc and explain their characteristics.
A Study on Air-Gap Flux Density Distribution of Bridged Slit-Stator Motors

This paper proposes bridged slit-stator motors to eliminate the complexity in the manufacturing process. Slit stator motors increase average torque and operational efficiency in comparison with conventional motors. However, such motors complicate the manufacturing process due to the insertion of slits. In the proposed motors, the slits are bridged to connect core parts of the stator. A magnetic circuit model is created from the current flowing through the stator. The air-gap flux density distribution is theoretically obtained. The effect of the bridges is clarified by theoretically and numerically obtaining the air-gap flux density distribution of the bridged slit-stator motors.
Comparative Evaluation of 800V DC-Link Three-Phase Two/Three-Level SiC Inverter Concepts for Next-Generation Variable Speed Drives

The adoption of wide band-gap (WBG) semiconductors is gaining momentum, particularly in industries where high efficiency and/or extreme power density are major concerns, e.g. electric transportation and aerospace. In order to fully leverage the advantages of WBG devices, identifying the converter topologies best exploiting their superior performance is of utmost importance. Hence, this paper analyzes and compares 2-level and 3-level SiC-based three-phase inverters for next-generation variable speed drives. Full sine-wave filtering at the converter output is assumed to counteract the negative effects of the fast switching transitions of WBG devices on the driven machine. The stresses on the active and passive components, i.e. semiconductor losses, output inductor flux ripple and DC- link capacitor RMS current, are calculated by analytical and/or numerical means. Moreover, the optimal semiconductor chip area, the power losses and the efficiency of each converter topology are investigated as functions of the switching frequency, providing a theoretical performance limit for each solution. Finally, a multi-objective optimization targeting an 800 V 7.5 kW system is carried out. The results are in good agreement with the theoretical performance analysis and provide an overview of the achievable efficiency vs. power density trade-off for all the considered topologies.
Investigation of Boresight and Jitter in Free Space Optical Communications with pointing error and Weak Turbulence

In this paper, we derive probability density function (PDF) of composite channel for free space optic system considering boresight and weak turbulence. Utilizing it, we also obtain average bit error rate (BER) expression of intensity modulation / direct detection (IM/DD). Lastly, we verify the analytical result by comparing it with Monte-Carlo simulation results and investigate the effect of boresight and jitter on average bit error rate (BER) with the numerical results.
On the Error Performance of Non-orthogonal Multiple Access Systems

Unlike orthogonal multiple access schemes, non-orthogonal (NOMA) ones have arisen as an appealing solution to meet the requirements of the upcoming era of massive connectivity. In NOMA schemes, users are allowed to restrainedly interfere. Therefore, NOMA’s ability to serve a number of users more than the number of available orthogonal channels has attracted tremendous research efforts in its different performance aspects. However, its error rate performance has not been sufficiently addressed yet. The currently available derived bit error rate (BER) formulas are either assuming ordered users based on their instantaneous channel gains or fit for special cases only. In this paper, assuming that the users are ordered in terms of the average channel gain and considering arbitrary number of users and modulation order, the average pairwise error probability (PEP) of downlink NOMA systems under Nakagami-m fading channels is derived. Both detection rules, maximum likelihood and successive interference cancellation, have been considered. The derived average PEP is then used to obtain the asymptotic diversity gain and an upper bound on BER using union bounding technique. Simulation results validate the accuracy of the derived formulas over different setups.
A Study on Event Identification on Social Media Data

This paper addresses the fact that there is no traffic tweet classification methods that try to identify the cause of the congestion. Our goal is to perform a multiclass classification of traffic-related tweets into traffic-congestion-cause-based groups. We perform various clustering techniques on our dataset, which we obtained from the Kolkata Traffic Police's Twitter handle. The clustering gives us the desired result of classifying the tweets into four broad categories based on the type of event causing the congestion.
CNN-LSTM-Network Based Robot Localization Using Spherical Images of Neighboring Places

The weakness of vision-based localization methods is that a robot may be confused when similar scenes are observed. In this paper we propose a method to cope with this problem by using multiple spherical images captured at neighboring places; that is, the ambiguity caused by visual similarity at a place is decreased by observing the scenes of its neighboring places. Concretely, the route that a robot moves along is sampled by discrete points which correspond to the places to be localized. Then, the localization problem is formulated as a classification problem, in which a place (a sampling point along a route) corresponds to a class. A CNN Long-Short-Term-Memory network is implemented to realize this task. The effectiveness of the proposed method is shown in the preliminary experiment.
Automatic Generation of Eye Gaze Corrected Video Using Recursive Conditional Generative Adversarial Networks

Eye contact plays an important role in conversations. However, it is difficult to maintain eye contact while using current popular video calling systems due to the different positions of the camera and display. To solve this problem, we introduce convolutional long short-term memory, which captures the features of frames up to the previous instant, into a deep learning generation model—conditional generative adversarial networks (GANs), which is recursive GANs—to generate eye gaze corrected video. By extending this network, the generator generates an image that takes the previous frame into account and the discriminator identifies the image by considering the previous frame. Thus, we aimed to achieve a consistent video conversion.
A Numerical Analysis of Improvement of Vibration Generator for Variable Frequency by Internal Capacitor Cancel Control Rectifier

Recently, energy harvesting systems focus on a vibration generator as one of the green energy sources. Since the vibration generators sends very low power, the performance improvement is required in order to use the power. Thus, our research group proposes an internal capacitor cancel control (ICCC) rectifier for the vibration generators. The proposed ICCC rectifier improved the output power. In general, the vibration is variable frequency. Therefore, this paper focuses on the variable frequency. This paper numerically reveals the improvement in the output power and analyzes current and waveforms to verify the effect of the proposed ICCC rectifier.
Face Synthesis via User Manipulation of Disentangled Latent Representation

In this paper, we propose a novel face synthesis method whose process can be conditioned not only on labels but also on latent variables as “unknown” labels corresponding to unrevealed factors. We extend Variational Autoencoder (VAE) without any additional networks to introduce conditional generation, disentangled representation, and adversarial learning into one autoencoder. Since previous conditional generative models require the annotation of labels to condition them on, disentanglement, i.e., the unsupervised discovery of generative factors enables users to generate face images more flexibly and more efficiently. Moreover, although generative adversarial networks (GANs) have problems of mode collapse and instability of the learning process, adversarial learning on VAE in an introspective way achieves both the variation of results and the stability of generation. Evaluations on the CelebFaces Attributes Dataset (CelebA) show that our method can generate face images following users’ conditioning both on the known and the “unknown” labels.
Interactive e-Book Linking Text and Multi-View Video

This paper proposes an interactive e-book which multi-viewpoint images and text are linked, to let user easily understand the contents such as catalogue or textbook which are composed of images and explanation. By displaying bullet-time video, it makes possible to enhance the visual information of book because it can present 3D information difficult to express in the 2D image, and the user will be able to freely determine the observation viewpoint. Two functions are designed to link the specific keywords of text and viewpoint of bullet-time video. These interactive functions can lead users to read the contents smoothly and quickly.
A Modified Min Sum Decoding Algorithm based on Approximation Enhancement for LDPC Codes

Min-sum algorithm(MSA) and its modification are used in LDPC decoder implementations because of its error performance and low computation complexity. In this paper a modified min-sum LDPC decoding algorithm through approximation enhancement using the smallest three minimum values is presented. The proposed algorithm improves error performance and reduces decoder iterations. From the simulation results for 5G NR LDPC codes, we show that the proposed algorithm can achieve about 1.6dB improvement of FER compared to the min-sum decoding algorithm and provide about 10% reduction of the average iteration numbers compare to NMSA with optimum normalization factor.
Code Coverage Similarity Measurement Using Machine Learning for Test Cases Minimization

Machine learning approach for minimizing the number of test cases on the test suite is an interesting research area on software testing. The research tries to minimize the number of test cases on the test suite by minimizing redundant test cases based on similarity classification. The Support Vector Machine, K-Nearest Neighbour, and Decision tree classify similar test cases by comparing the lines executed by test cases. The result has shown that the support vector machine is the highest score on accuracy and the lowest score on error rate comparing with K-Nearest Neighbour, and Decision tree. Minimize the redundant test cases increase the quality of the test cases, and reducing time on the testing process.