Miracle-dz/UniFAR-FTUs huggingface dataset:
Design Optimization and Reliability Enhancement of Community Based Islanded Microgrid through Frequency Stability

This paper investigates a new strategy for voltage and frequency control of isolated and autonomous Microgrid. In this study Microgrid consists of energy storage, renewable energy based sources that consists of solar pv, microhydro and Biomass with their respective power electronics converters. We have chosen multiple options and tried to find-out the trade-off among cost, stability, reliability and the availability of renewable resources. Strictly, for more stable and reliable operations, cost would be higher. Thus, we have proposed design of an off-grid solution such that all the power sources i.e. solar pv, biomass and microhydro will be stored and supplied through batteries using converters in order to ensure frequency stability for PV, microhydro and biomass. We have considered a two pronged approach i.e. 1) switch battery banks to dump the excess energy and maintain the frequency of the system (similar to dummy loads in electronic load controller – an electronic governor for microhydro). 2). Energy from microhydro can either be used directly or through batteries. Where, the biomass will always be used from batteries and as a backup source. System has been modelled for different scenarios by using MATLAB/SIMULINK. The results verify that system can work in stable state under changing conditions.
Quantitative Evaluation of Voronoi Graph Search Algorithm in UAV Path Planning

The Voronoi graph search algorithm is a classic method of UAV path planning. There have been many related mature researches in this field, but most of them are about the design and implementation of this algorithm, few studying the influence of each parameter on the performance of the whole algorithm. In this paper, the establishment of a quantitative model of UAV path planning, algorithm design, performance evaluation and optimization suggestions are discussed, and the impact of the threat proportion and the number of threats on the planning results of this algorithm is mainly studied. Furthermore, we analyze the time distribution among Voronoi graph founding time, Dijkstra planning time, and path smoothing time. The evaluation methods and optimization advice of Voronoi graph search algorithm are given, which can be adopted to other path planning algorithms as well.
Dual Channel Coherence Based Speech Enhancement with Wavelet Denoising

The most essential work in a communication system is reconstructing a clean speech from a noisy environment which is a challenging problem in speech processing. The performance of speech processing system could increase by utilizing an effective speech enhancement technique in case of mobile phone, hearing aids, etc. An innovative speech enhancement technique may help in increasing the accuracy of speech recognition methods. In this paper a new approach has been introduced which deals with the implementation of dual channel speech enhancement technique utilizes the coherence function computed from the dual input signals without prior noise statistics for noise reduction. For post-processing, discrete wavelet transform (DWT) is used as a gain function which is set up by SNR values using coherence function. The proposed algorithm is applied in an environment where interfering speakers are present and gives a better result than other algorithms when the speech is corrupted by different noise types.
Research on Fuzzy Rule-Based Reasoning System for CC Quality Assurance

High-quality slab Continuous Casting (CC) production technology is one of the most competitive core technologies in the steel industry. How to inherit and apply the experience of CC expert has become an urgent problem in the quality assurance of CC. First, analyze the demand of the entire CC quality assurance system, carry out the system-based overall design and functional module decomposition design, design the system's workflow. Next, according to the characteristics of different types of knowledge in the field of CC, the representation form of design knowledge, construct the knowledge base of CC field, design the rule reasoning control process in detail according to the functional requirements of the rule inference system, and manage the knowledge base of CC field. Then it designed with maintenance. Finally, the human-computer interaction interface of the system is designed to complete the development of the system, and the detailed function test and analysis of the system's implementation effect are carried out. The system application results show that the system can well represent different types of CC quality assurance knowledge and apply it to systematic reasoning, which can solve the common quality fluctuation problems in CC production process and meet the application requirements. It improves the level of intelligent production of CC, and has certain promotion and reference value for the field of intelligent manufacturing.
Design of Robust PID Controller for Level Control of CSTR with Safety Interlocks

In this paper, a robust PID controller is designed with safety interlocks to control the level of liquid in continuous stirred tank reactor (CSTR). Designed PID controller is continuous and cost effective compared to PLC and DCS. The designed PID is suitable for small scale industries where cost is constrain. For tuning of the PID Ziegler-Nicholas, open loop tuning method is used. The designed PID is tested on process plant to control the level of liquid in CSTR
Improving Polar Codes by Spatial Coupling

In this paper, spatial coupling technique is used to improve the error-correcting performance of finite length polar codes. Polar codes are considered as base codes of coupling. Two types of spatial-coupling methods are investigated. For the first coupling method, some message bits of a polar base code at each coupling position are used as frozen bits of one-side adjacent polar base code. For the second spatial-coupling method, multiple polar codes are associated by combining message bit blocks of adjacent coupling positions with modular-two addition. Simulation results show that, with a little extra iteration complexity, the two types of spatially coupled polar codes can provide better error-correcting performance than original polar codes. When setting the coupling ratio at 0.33 and coupling pattern as Uncertain-Certain, the first spatial-coupling method provides the best performance output. The second spatial-coupling method with coupling width 3 provides better performance than that with coupling width 2. Furthermore, the first spatial-coupling method with short message block length provides better decoding performance, and the second method with large message block length performs better.
Chinese Named Entity Recognition Based on CNN-BiLSTM-CRF

Named Entity Recognition (NER) is an important basic task in natural language processing (NLP). In recent years, the method of word representations enhancement by character embedding has significantly enhanced the effect of entity recognition. However, this kind of character embedding method only works on alphabetic spelling words such as English, and the same method is not suitable for Chinese. Aiming at the inherent characteristics of Chinese as morpheme writing, we propose a novel neural network model based on CNN-BiLSTM-CRF in this paper. Convolution neural network (CNN) extracts the glyph embeddings with morphological features from each Chinese character, which are concatenated with the character embeddings with semantic feature information and fed to the BiLSTM-CRF network. We evaluate our model on the third SIGHAN Bakeoff MSRA dataset for simplified Chinese NER task. The experimental results show that our model reaches 91.09% in F-scores which does not rely on the hand-designed features and domain knowledge.
An Efficient Way of Anonymization Without Subjecting to Attacks Using Secure Matrix Method

In current times huge data evolving from multiple sources like hospitals, reservation agencies, online transactions, etc. in massive volumes and obtaining in various forms. These data have privacy concerns due to leakage of data. The outgrowths raised in this situation may drive towards anonymization of sensitive identity information. Let a dataset released for the research purpose by removing the identifying attributes and sensitive attributes, but an adversary find to disclose the identity of the individuals by using the quasi-identifiers and non-sensitive data. Anonymization methods are classified into k-Anonymity, 1-diversity, and t-closeness fail in the better way of hiding the data. These techniques lead to a homogeneous attack, background knowledge attack, and similarity attack. In this article, novel method has been proposed based on secure matrix methods for an effective way of hiding the critical data. This technique accepts the non identified data as an input and produces anonymized data as an output without subjecting to attacks. It experimentally produces better results in anonymizing the data with less execution time.
On the Use of U-Net for Dominant Melody Estimation in Polyphonic Music

Estimation of dominant melody in polyphonic music remains a difficult task, even though promising breakthroughs have been done recently with the introduction of the Harmonic CQT and the use of fully convolutional networks. In this paper, we build upon this idea and describe how U-Net- a neural network originally designed for medical image segmentation - can be used to estimate the dominant melody in polyphonic audio. We propose in particular the use of an original layer-by-layer sequential training method, and show that this method used along with careful training data conditioning improve the results compared to plain convolutional networks.
SIMBA: A Semantic-Influence Measurement Based Algorithm for Detecting Influential Diffusion in Social Networks

In recent years, social networks have been the gate for the explosion of thousands of data sets. Interrelations and influence between participants expand dramatically due to the success of online social networks such as Facebook, Foursquare, Twitter and others. The challenge of identifying influential nodes, has been playing a significant part in the scientific community revealing new research opportunities in many application domains such as recommendation systems, viral marketing and others. Most of the research done considered only the network structure with its geospatial information ignoring the importance of semantics underlying these datasets. In this paper, we propose a semantic-influence measurement based algorithm (SIMBA) that efficiently detects influential nodes on social networks. SIMBA is based on both the geospatial information as well as semantic information associated with each user. SIMBA is used for location recommendation purposes and also in examining the power of influential nodes in directing the recommendation to other connected nodes. Different experiments have been conducted under different conditions and varying parameters to verify and validate the proposed algorithm against the latest published algorithms. Two real location based social networks namely; Brightkite11https://snap.stanford. edu/data and weeplaces22http://www.yongliu. org/datasets are used in the experiments.
Design of a Novel Piezo-Driven XYZ Compliant Parallel Micro/Nano-Positioning Stage

This paper presents the design and optimization of a symmetrical compliant XYZ parallel stage for micro-/nano-positioning, which is driven by three piezoelectric actuators (PEAs). A compact design is devised by introducing serial-parallel 3-DOF decoupling unit and spatial compound bridge-type amplifiers. Structural parameter optimization has been conducted via finite-element analysis (FEA)-based multi-objective genetic algorithm, which produces a compact physical size of the XYZ stage. The performance of the optimized XYZ stage under different loads are evaluated by FEA simulation study. Under free-load condition, the stage is able to provide a workspace of 143.10 × 143.12 × 135.18 μm 3. The maximum horizontal cross-axis error is only 0.01%, while the vertical cross-axis error is 5.54%, which is caused by the gravity effect. The loading test result reveals that the maximum bearable load of the stage is up to 74.46 N, i.e., 7.6 kg.
Voice Controlled Navigational Aid With RFID-based Indoor Positioning System for the Visually Impaired

This paper presents the development of a voice-controlled navigational system that could assist the visually impaired people in travelling indoors independently. The system provides audible directions to the users in navigating from a specific location going to the desired destination. This system uses passive radio frequency identification (RFID) tags strategically located along the hallways of the facilities. These tags contain audible directions and they are detected by an RFID reader attached at the tip of a customized cane. The reader is connected to the Raspberry Pi 3 that serves as the brain of the system. It processes the data from the reader, validates the voice input of the user and provides the audio response. The adjustable customized cane measures from 88 cm to 130 cm with an approximate weight of 165 grams, which is lighter than commercially available canes that weigh around 300 grams. The speech recognition feature of the system is evaluated and an accuracy rate of 75.76% is obtained. Its response time in reading the tags is 1.38 seconds for a maximum distance of 1 inch. Overall, the system runs autonomously which helps the visually impaired people in navigating independently inside a specific establishment.
NSCT-based Adaptive Illumination Processing for Face Recognition

A NSCT-based method is presented for adaptively extracting illumination invariants of face images, for the purpose of removing the effect of lighting variations on the algorithm for face recognition. This study begins with the use of logarithm transform to face images, and the transformed images are then decomposed into low-frequency sub-bands and high-frequency directional sub-bands by NSCT. After that, the adaptive threshold of each band is found out according to the distribution of NSCT coefficients in high-frequency sub-bands, with which the sub-bands are filtered by a compromised threshold function. Finally, inverse NSCT is applied to the filtered sub-bands, with the illumination invariant obtained. Experimental results on the two well-known face databases of Extended Yale B and CMU PIE show that the proposed algorithm can effectively eliminate the effect of illumination on face images with satisfactory recognition rates.
Critical Periods in Vision Revisited.

For four decades, investigations of the biological basis of critical periods in the developing mammalian visual cortex were dominated by study of the consequences of altered early visual experience in cats and nonhuman primates. The neural deficits thus revealed also provided insight into the origin and neural basis of human amblyopia that in turn motivated additional studies of humans with abnormal early visual input. Recent human studies point to deficits arising from alterations in all visual cortical areas and even in nonvisual cortical regions. As the new human data accumulated in parallel with a near-complete shift toward the use of rodent animal models for the study of neural mechanisms, it is now essential to review the human data and the earlier animal data obtained from cats and monkeys to infer general conclusions and to optimize future choice of the most appropriate animal model. Expected final online publication date for the Annual Review of Vision Science, Volume 8 is September 2022. Please see http://www.annualreviews. org/page/journal/pubdates for revised estimates.
Research on Gravity Compensation Algorithm of Model-free Manipulator Based on L1-norm Convex Optimization

In this paper, a model-free gravity compensation algorithm for manipulators based on $L$1-norm convex optimization is proposed. The method does not depend on the structure and parameters of the robotic arm. In contrast, this method optimizes the solution of gravity compensation parameters based on the sampled data. Theoretical analysis shows that the reconstruction of the gravity compensation parameters of the manipulator is realized based on the joint angle and joint torque training data. Experiments show that the gravity compensation results can restore the joint torque of the test set well, which proves the effectiveness of the method.
Modeling and Control of Battery Energy Storage System for Providing Grid Support Services

Managing intermittency and uncertainty caused by large scale penetration of renewable energy is a challenge in maintaining the real-time operation of a power system. A Battery Energy Storage System (BESS) has shown promising results in maintaining the reliability and operation of a reduced inertia power system by providing grid support services. A BESS can be modeled to alleviate frequency-voltage variations which results due to imbalance between generation and demand in a power system. This paper demonstrates the modeling and control of a grid size BESS for providing primary and secondary frequency response and voltage support. A simple BESS model is developed while maintaining the detailed behavior of BESS in the power system. The model is developed as a user written model in a commercial transient stability program PSLF. A technical overview of BESS is illustrated along with the various modes of operation and operational constraints like State of Charge (SoC), charge and discharge rate. The proposed positive sequence transient stability BESS model results are validated with electromagnetic transient (EMT) based program like PSCAD/EMTDC.
Attentive Neural Network Recommendation for E-Commerce Based on Various Implicit Feedback

With the Internet applications and information exploding exponentially, recommendation system has become an effective measure to solve information overloading. The traditional recommendation algorithm for users' implicit feedback is mainly based on collaborative filtering and Bayesian ranking methods, which are not fully utilize implicit feedback features such as click, collecting, adding to shopping cart and purchase. In order to make full use of various implicit feedback in the e-commence scenario and capture the dynamic temporal features of user-item interaction, this paper proposes a recommendation model which integrates use-embedding vector representation and self-attention mechanism. We conduct comparative experiments on public datasets from e-commerce websites. The evaluation results show that compared to the traditional recommendation models and other neural network models recently proposed, our model has better performance and generalization ability.
An Efficient Strategy for Applying Compute-and-Forward to the MARC

With the aim of improving network throughput and achieving full diversity gain, this paper focuses on strategies for applying compute-and-forward (CF) scheme to the multiple access relay channel (MARC). The direct application of the original CF to the MARC results in poor error performance due to the probability of a rank deficient coefficient matrix, which is an inherent problem of a CF system. One way to solve this problem is by allowing the relay and the destination to fully cooperate with each other in constructing a full rank coefficient matrix. However, this requires a large amount of communication overhead. This paper proposes an efficient strategy where the destination always attempts to decode transmitted messages by itself, without the help of the relay. The destination cooperates with the relay only when necessary. It is shown that with a small amount of overhead, the proposed strategy outperforms the existing approaches in terms of achievable sum-rate, outage probability, and throughput. Furthermore, the proposed strategy also achieves the full diversity gain of the MARC.
Efficient Mobility-Aware Task Offloading for Vehicular Edge Computing Networks

Vehicular networks are facing the challenges to support ubiquitous connections and high quality of service for numerous vehicles. To address these issues, mobile edge computing (MEC) is explored as a promising technology in vehicular networks by employing computing resources at the edge of vehicular wireless access networks. In this paper, we study the efficient task offloading schemes in vehicular edge computing networks. The vehicles perform the offloading time selection, communication, and computing resource allocations optimally, the mobility of vehicles and the maximum latency of tasks are considered. To minimize the system costs, including the costs of the required communication and computing resources, we first analyze the offloading schemes in the independent MEC servers scenario. The offloading tasks are processed by the MEC servers deployed at the access point (AP) independently. A mobility-aware task offloading scheme is proposed. Then, in the cooperative MEC servers scenario, the MEC servers can further offload the collected overloading tasks to the adjacent servers at the next AP on the vehicles’ moving direction. A location-based offloading scheme is proposed. In both scenarios, the tradeoffs between the task completed latency and the required communication and computation resources are mainly considered. Numerical results show that our proposed schemes can reduce the system costs efficiently, while the latency constraints are satisfied.
Towards Control of EEG-Based Robotic Arm Using Deep Learning via Stacked Sparse Autoencoder

Recently, an EEG-based robotic arm control has offered a key solution to the problem of high level amputation or severe neuromuscular damage. Several attempt to use EMG-based pattern recognition (PR) failed due to insufficient myoelectric signals from the residual limb to perform control functions. The EEG activity recorded from human scalp is used to control the movement of a robotic arm. This can either be achieved when the arm is attached to or separated from the amputee stump by interfacing the brain directly to the robotic arm through the brain-machine interface (BMI). To build an intelligent robotic (or prosthetics) system that will manipulate object seamlessly with multiple degrees of freedom (DoF), it is required that a robust learning algorithm which is able to control the prosthetic arm while interacting with the environment should be implemented. However, the conventional machine learning approach of using handcrafted features to design a robot controller that can perform multiple task is not a feasible option. Hence, we propose a robust learning control which is based on unsupervised learning algorithm of deep autoencoder. We applied stacked autoencoder to generate our features, and softmax layer is then used to classify five different motor imagery tasks. The proposed method produced an overall accuracy of 98.9% across the four amputees recruited for the experiments. Our algorithm shows a better performance when compared with the state-of-the art classifiers. Thus, the proposed results demonstrates the possibility of providing better control performance for EEG-based prosthesis.