Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-synchronous Generation Impact on Frequency Response – A case from Albania

Albanian electric power generation has been almost totally dependent on hydropower since its system inception. Within the past decade, although modest, there has been some share of power produced from Renewable Energy Sources (RES), mostly from Solar Power Plants. Going forward, the Albanian government has promoted renewable energy as an important part of its energy mix and is planning to increase the share of injected RES composed of hydro, Photovoltaic (PV) and Wind. Among these, wind energy is one of the most promising sources. This paper gives an overview of the studies performed regarding System Frequency Response (SFR) by replacing hydro inertia with non-synchronous generation. A study of frequency behavior under several penetration levels of non-synchronous generation is presented with cases which show how system frequency is affected when replacing hydro-power inertia with wind turbine converter inertia
Research on Urban Load Rapid Recovery Strategy Based on Improved Weighted Power Flow Entropy

As the final stage of power system restoration, the critical task of load restoration is to restore the remaining load as quickly as possible. With the continuous increase of the temperature-controlled load and the proportion of electric vehicle load in the urban power grid, the complexity of the load side in the restoration process gradually increases. Therefore, based on the existing grid environment, this paper considers the sudden increase in load recovery caused by cold load pick-up and the auxiliary effect of electric vehicle discharge on load recovery during the load recovery process. From the perspective of economy, safety, and speed, this paper establishes a multi-objective function that includes the amount of load, improved weighted power flow entropy, and the number of recovered lines. The multi-objective evolutionary algorithm based on decomposition is used to optimize the constructed multi-objective load recovery model. Through the IEEE30 node system, it is verified that the method proposed in this paper can effectively establish a fast and safe load recovery plan that meets the actual grid environment.
Correlation Analysis of Causes of Railway Accidents Based on Improved Apriori Algorithm

Studying the railway accidents that occurred in the past and discovering the potential causality among the factors that affect the accidents can effectively improve the safety of the railway system and reduce the occurrence of railway accidents. In order to dig out the law of the effects among the causes of railway accidents, an improved interestingness measure (Confidence_interestingness, C_Inter) is put forward in this research, which overcomes the shortcomings of the traditional apriori algorithm that ignores negative association rules. At the same time, the weights of different levels of accidents are determined in this paper. By combining the severity of railway accidents, the causative factors of railway accidents with serious consequences can obtain higher attention. The result of the example shows that, compared with the traditional association rule algorithm, the weighted association rule algorithm based on C_Inter can mine the positive and negative association rules with actual value to a greater extent, and provide more accurate and comprehensive reference basis for the railway administration to make the accident prevention mechanism.
Making the Sourcing Decision of Software Maintenance and Information Technology

Outsourcing has been getting a significant growth for the last few years. Organizations tend to outsource Information Technology (IT), primarily to take advantage of the availability of qualified, trained and skilled workforce in low cost countries across the globe. Outsourcing of IT and software maintenance seem very promising, but a number of factors, risks, and challenges associated with the outsourcing process that make the sourcing decision very complicated. The present study aimed at gaining in-depth understanding of the three aspects of outsourcing, namely; perceived benefits of IT outsourcing, influencing factors of IT outsourcing and software maintenance offshoring. The findings of the current study will lead us to develop a sourcing framework for outsourcing decision as well as a decision support system for software maintenance. A systematic literature review is performed that presents perceived benefits of IT outsourcing, the influencing factors of IT outsourcing and software maintenance. Furthermore, the identified factors are analyzed based on their occurrences in literature as well as chi square test is performed to derive the significant differences amongst the factors based on decades. Similarly, critical success factors are derived both for IT outsourcing and software maintenance offshoring. Our article shows that how the critical success factors impact the IT as well the software maintenance in global delivery perspective. The findings of the current study will help the IT experts and decision makers in making suitable sourcing decisions.
Unidirectional Three Phase Dual Active Bridge DC-DC Converter Used in Electrical Vehicles

This work focuses on the unidirectional high frequency three phase dual active bridge dc-dc converter. This type of converters is used in sustainable energy generation and it is suitable for the electrical vehicle application. The three-phase dual active bridge dc-dc converter consists of two power conversion stages (an inverter and a rectifier) separated by three single phase high frequency planar transformers in order to provide a galvanic isolation between the input and the output. A phase shift control strategy is applied between the signal gates of the switches of the two active bridges in aims to control the transferred active power from the input port to the output. Thanks to the three-phase structure and the high frequency operation, this system is characterized by low input current ripples and it has the advantage of its reduced size. Moreover, this converter provides a high voltage gain and a wide input voltage range with an important efficiency. In this paper, the modeling and the control of the dual active bridge dc-dc converter will be clearly explained and simulations results under MATLAB/SIMULINK will be shown to validate the operation of the converter topology. Finally, detailed analysis will be carried out.
Security and Power Management in IoT and Fog Computing: A Survey

Internet of Things (IoT) is a cooperative of small sensors and actuators and any other small devices that provide control and monitor for many aspects in real life via internet. These devices provide big data that need a high capabilities system to serve such as cloud computing. However, cloud computing itself cannot satisfy all of IoT requirement such as low latency, mobility, etc. To solve this problem, researchers proposed to add new layer called fog computing layer that sit between IoT layer and cloud computing. Fog computing is a micro data center that support cloud computing to satisfy IoT requirement. Fog computing is still in the infancy level and has many challenges that need to be resolved. In this paper, a survey that focus on power management and security behavior in both Fog computing and IoT has been conducted. Although there are many proposed methods and techniques in these fields, the survey showed that both systems suffer from weaknesses in security and power management and need to be improved.
Review of Healthcare Database

Digitalization has an impact on the healthcare system too and the result can be seen in improvements in the medical field. All healthcare components like doctors, hospitals, clinics, diagnostic centers are working together to increase life expectancy using advanced digital tools. Method of diagnosis is also changing as decisions by doctors are based on case study and analysis rather than individual perception. So to deal with more perfection towards treatment and to apply reliable methods for patient more consistent way of recording data, status and stage of diagnosis is needed. Databases generated at each stage need to be stored, secured, compatible, standard and useful for diagnosis as well as preventive healthcare. The procedures for receiving medical care through digital medium is under drastic change in most of the nations, healthcare professionals need to adapt it for better outcomes. The new regulations now encourage saving patient records or databases for the services rendered in a given format like EHR or EMR. In this paper review is done to study current developments in the medical field related to digitalization. And it is observed that a huge amount of biomedical data generated through digitalization needs to be optimized. And also to make possible the interoperability of individual dataset and integration of clinical and biomedical data into biomedical data science.
Power and Variation Improved Near-Vt Standard Cell Library for 28-nm FDSOI

In this work, a new library is developed for 28-nm FDSOI CMOS technology. The new library is optimized for near-threshold operating voltage of 0.4V by balancing the pull-up/ pull-down networks (PUN/PDN) of logic gates, focusing on device sizing and exploiting poly-biasing feature. With the new library, up to 38% of leakage power consumption savings and up to 9% of dynamic power consumption savings are achieved for a variety of ITC benchmark circuits and an ARM Cortex-M0 as compared to the existing library. Combining the newly developed library with the existing libraries reduces the power consumption even more without any performance and area penalty. Monte Carlo simulations on the critical path delay of the ARM Cortex- M0 shows that the mean and standard deviation decreased by up to 8% and 22%, respectively, improving speed and robustness.
Research on Detecting Bearing-Cover Defects Based on Improved YOLOv3

Detecting defects, which is a branch of target detection in the field of computer vision, is widely used in factory production. To solve the problems in existing detection algorithms that relate to their insensitivity to large or medium defect targets on bearing covers, their difficulty in detecting subtle defects effectively and their lack of real-time detection, in this work, we establish a large-scale bearing-cover defect dataset and propose an improved YOLOv3 network model. The proposed model is divided into four submodels: the bottleneck attention network (BNA-Net), the attention prediction subnet model, the defect localization subnet model, and the large-size output feature branch. To test the generality, robustness and practicability of the new model, we design a comparative experiment under abnormal illumination conditions. We design an ablation experiment to verify the validity of the proposed submodules. The experimental results show that our model solves the problem of the YOLOv3 algorithm’s insensitivity to medium or large targets and satisfies real-time detection conditions. The mAP result is 69.74%, which is 16.31%, 13.4%, 13%, 10.9%, and 7.2% more than that of YOLOv3, EfficientDet-D2, YOLOv5, YOLOv4, and PP-YOLO, respectively.
Two Stage Emotion Recognition using Frame-level and Video-level Features

This paper compares a seven class classifier with a two stage classification for categorical emotion recognition. We use the Multimodal Emotion Recognition (MER) Dataset of the FG2020 competition which apart from video contains skeleton data collected with a Microsoft Kinect. We compare the performance of different unimodal features as well as various combinations of multimodal features. We also compare frame-level features with video-level features. We achieved 50parcent accuracy using multimodal video-level features and two stage classification on one hand, 49 % accuracy is achieved with the seven class classifier on the other hand.
Cooperative Bicycle Localization System via Ad Hoc Bluetooth Networks

Bicycles are becoming increasingly more equipped with embedded connected devices, by design or through after market products, to support applications such as fitness monitoring and tracking. Bluetooth (BT) and BT Low Energy (BLE) technology is often embedded in such devices to support connectivity to a personal mobile device or a dock, when parked. BT/BLE transmit periodic beacons for node discovery that can be explored for $\mathrm{V}2\mathrm{X}$ applications, such as safety and fleet management. We present a distributed system that explores periodic BT beacons sent by a module embedded in a bicycle to opportunistically locate nodes of interest (NOI). We address the particular application of stolen bicycle detection. In a scenario in which a bicycle is stolen and has its communication system tampered with but BLE remains functional, a service provider (e.g., fleet operator, authorities) is informed of this new NOI and shares an updated NOI list with the NOI detection-enabled bicycles. In turn, the bicycles flag contacts with stolen bicycles to the provider backoffice, at the earliest convenience (depending on available communications interfaces: immediately if cellular is available, or opportunistically when passing by a dock). We describe the operation and software architecture of the system, and an actual implementation in COTS equipment. Experimental measurements of the communication range and a demonstration of the system for oroof-of-concept are also reported.
Genomic Data Analyzing Workflow for Single Nucleotide Polymorphisms in Human Nervous System Cancers

Mutation data analysis related to the disease is one of the main objectives in genomic data analysis. Analyzing Single Nucleotide polymorphisms (SNPs) are time-consuming and intensive labour required process. Moreover, finding all relevant data for the mutation is also a complicated task. Cancer is a genetic disease which begins by accumulating mutations in an organ. Cancer genesis can be explored by analyzing genomic mutations and protein changes.Our objectives of this study are to build a user-friendly platform for data conversion, data enrichment and SNP data interpretation in human nervous system cancer mutations based on R programming. We have used 15 datasets to develop the workflow. R packages, UniProt conversion tool, Ensemble Genome conversion tool and VEP tool were used for the enrichment. Data enrichment was performed via using data in DBSNP, COSMIC, Clinvar and Uniprot databases. Mutation protein effect prediction was performed with SIFT and PolyPhen. Data filtration and SNP mutation analysis were completed with R packages and MS Excel.Initially, we performed data purification step which relies on genome build, database version and genome position. According to the results, the best method for genomic mapping is chromosome position mapping. Secondly, we enriched mutations with genomic data. We filtered protein mutated genomic level mutations. Protein position identification and data enrichment were the final steps of the workflow. We designed a reusable workflow for large mutation datasets analysis via using human nervous system cancer mutation data. As future work, we will try to merge other cancer datasets for this workflow.
Multi-objective vehicle path planning method for recycling process of used electrical and electronic products

During the recycling process of used electrical and electronic products from recycling places to sorting center, the vehicle path planning is an urgent problem to be solved which needs to minimize the number of vehicles and vehicle transportation distance simultaneously. To solve this problem, a multi-objective optimization algorithm is proposed which combines the genetic algorithm (GA) and multi-objective evolutionary algorithm based on decomposition (MOEA/D). Firstly, the vehicle path planning model of the sorting center is established according to characteristics of product transportation process. Secondly, the GA is used to produce some vehicle path planning schemes. Then, the MOEA/D is introduced to deal with the trade-off relationship between two objectives. Simulation results demonstrate that the proposed algorithm is able to generate a set of non-dominated solutions.
Typical and Reverse Other-Race Effect on Tibetan Students’ Emotional Face Recognition

We examined whether an other-race effect (ORE) exists in recognizing own groups and other ethnic groups emotional faces and whether such an effect depended on one’s individual experience level. We used the learning–recognition paradigm and recruited 80 Tibetan students for each experiment to recognize the emotional faces of Tibetan and Han nationalities. Different types of ORE were observed when individuals recognize different emotional faces. On the one hand, positive emotional faces resulted in typical ORE, which was not influenced by an individual experience level. On the other hand, negative emotional faces resulted in typical ORE for low-experience individuals but resulted in a reverse ORE for high-experience individuals. Therefore, the recognized and identified emotional faces may differ in terms of how they are processed. In addition, the recognition of patterns from these images may be influenced by one’s individual experience level.
Review of computational approaches to Parkinson’s disease gene prediction

Parkinson's disease is a chronic neuro-degenerative disease that affects the central nervous system. Since the causes of the disease are still unknown, both genetic and environmental factors are believed to be involved. Therefore, the identification and prediction of this genetic disorder plays an important role in early detection and treatment. The genomic data is extremely large and distorted, so the use of R tools can make the analysis and pre- processing of data easier. There are several computational approaches to the prediction of this disorder which varies with its accuracy and performance.
Integrated Hybrid Antenna Based on Spoof Surface Plasmon Polaritons

In this paper, we proposed an integrated hybrid antenna based on spoof surface plasmon polaritons (SSPPs) which combines with omnidirectional antenna (ODA) and the frequency beam scanning antenna (FBSA). The ODA is fed by the SSPPs and the operating principle of FBSA is based on the higher-order modes of the SSPPs. The radiation mechanisms of the proposed integrated antenna are that the low-pass transmission modes of SSPPs are used to excite the ODA and the higher-order modes of SSPPs are utilized to generate the frequency beam scanning waves. That is to say, the SSPP design not only serves as the feeding structure for the ODA, but also acts as the radiating element for the FBSA. Simulated results show that the proposed integrated antenna achieves two impedance bandwidths. Within the operating bandwidth 2.32 GHz to 4.42 GHz, the proposed integrated antenna achieves omnidirectional radiation patterns, which enables the simultaneous use for wireless local area network (WLAN) and 5 G applications. Meanwhile, from 11.45 GHz to 25.5 GHz, the proposed integrated antenna acts as a wide-angle frequency beam scanning characteristic and can be used for planar integrated communication systems. Additionally, a prototype of the proposed integrated hybrid antenna is fabricated and tested. The measured results have a good agreement with the simulated ones.
Die-to-Die Testing and ECC Error Mitigation in Automotive and Industrial Safety Applications

Two significant trends can be nowadays seen in automotive and industrial applications: an increase of the amount of data that is stored and elaborated by those systems, thus requiring bigger on-board DRAMs (Dynamic Random Access Memories), and the strict demands for reliability and safety. Safety is ruled by standards, that impose requirements for acceptable FIT rate–one of the most common metrics used for quantitatively evaluating the effects of such errors. It is then relevant to investigate the errors that can occur in DRAMs and propose mitigation techniques to deal with them. In this paper, die-to-die testing scenario is considered, and a methodology is described for mitigating the effects of errors by using well-known Error Correcting Codes (ECC). An advanced ECC solution is then presented along with the infrastructure needed for effectively testing DRAMs, including soft errors and permanent faults. The FIT rates calculations are finally considered, together with examples and case studies illustrating the effectiveness of the proposed solution.
Classification of Parkinson's Disease using Speech Attributes with Parametric and Nonparametric Machine Learning Techniques

Parkinson’s disease is a neurological disorder that affects the central nervous system which results in improper functioning of body movements including difficulty in speaking, walking, rigidity, and so on. The aim is to classify Parkinson’s disease based on acoustic voice features in dysphonic speech disorder in Parkinson’s patients using state of art machine learning algorithms. Some of the parametric and nonparametric machine learning techniques was used on the voice features dataset. This research aims to evaluate the performance of state of art algorithms such as the Naïve Bayes Algorithm, Logistic regression, K-Nearest Neighbors, and Random Forest Algorithm to detect Parkinson’s disease which gives higher classification accuracy. The performance is evaluated by pre-processing the data based on speech attributes and fed into the machine learning models for further processing. Various performance metrics like precision, recall, F1 score are computed for all four machine learning techniques and the results show that nonparametric models using Random Forest and K-Nearest Neighbors produce higher classification accuracy of 87.2% and 90.2% compared to parametric models.
An RFID Enabled Miniaturized Chipless Tag for IoT Applications

In this research paper, a Novel, compact, low cost and entirely passive Chipless radio frequency identification tag are presented. The tag design comprises of 10 half cut cross loop-shaped resonant elements where each resonant element is represented by one encoding bit in the frequency domain. The tag has a compact geometrical footprint of 10.8×10.8mm2. The electromagnetic performance analysis of the tag is done by using multiple substrates Rogers RT/Duroid/5880, Rogers RT/Duroid/5870 and Taconic TLX-0. The Physical dimension of the proposed tag design is relatively small with very high bit capacity of 8.57 bits/cm2 and spectral efficiency of 2.5 bits/GHz. The proposed tag design possesses the ability to generate 210 unique IDs for the identification process of various objects. The proposed tag operates in license-free UWB band having a frequency band of 3.5 GHz to 7.5 GHz. The proposed tag is appropriate to be deployed in the field of Internet of Things.
Global Prescribed-Time Controller Design for p-Normal Switched Nonlinear Systems With Dead-Zone Inputs

The global stabilization controller design is addressed in this paper for a kind of uncertain switched nonlinear systems (SNSs) in $p$ -normal form. Notably, the study possess two significant features: the system under investigation is subject to the dead-zone inputs, and the system states are expected to be driven to zero in prescribed finite time. To do this, a novel time-varying scaling transformation (TVST), which can be used to change the original prescribed-time stabilization into the asymptotic stabilization of transformed one, is first recommended. Then, with the aid of the common Lyapunov function (CLF) technique, the state feedback stabilization within prescribed-time of SNSs is established by using the recursive idea. Finally, the effectiveness of given control method is confirmed by simulation results of a liquid-level system.