Miracle-dz/UniFAR-FTUs huggingface dataset:
Switch Open-circuit Fault Diagnosis and Fault-Tolerant Control Strategy for DC-DC Converters

This paper presents a simple and fast switch open circuit fault (OCF) diagnosis and fault-tolerant control strategy for non-isolated DC-DC converters. The fault diagnosis strategy, based on the change in the inductor current, uses a simple digital counter logic circuits for fault diagnosis and fault-tolerant control. The proposed method is tested on buck, boost and buck-boost topologies of the DC-DC converter. However, the method can be extended for other topologies of non-isolated dc-dc converter. The converters along with the proposed fault diagnosis and the reconfiguration strategy are modeled and simulated using the MATLAB Simulink environment. The robustness of the method against abrupt variations in the load as well as in the source voltage is examined. The results confirm that the proposed fault diagnosis and the reconfiguration strategy for the converter is effective in terms of simplicity, fast fault detection with accuracy and good voltage regulation.
TransPCFG: Transferring the Grammars From Short Passwords to Guess Long Passwords Effectively

Long passwords are gaining popularity in password policy recommendations; however, data-driven guessing studies are woefully inadequate in adapting to long passwords, lacking in both guessing efficiency and their composition guidelines. For state-of-the-art data-driven password guessing methods such as PCFGs (Probabilistic Context-free Grammars), their guessing efficiency is limited by the presence of a large scale training data, or the lack thereof. Given that long passwords leaked in the real world are typically scarce, coupled with the fact that the data-driven methods’ performance depends on training data, obtaining good performance on long passwords has become a key challenge. To overcome the dataset limitation, we propose a framework <italic>TransPCFG</italic>, that transfers the knowledge, (i.e., grammars in PCFGs), from short passwords to facilitate long password guessing. We further perform an empirical evaluation based on three real-world datasets and the results demonstrate superior performance over the state-of-the-art data-driven guessing methods under <inline-formula> <tex-math notation="LaTeX">${10}^{14}$ </tex-math></inline-formula> offline guesses. For passwords with 16 characters, <italic>TransPCFG</italic> can compromise an average of 23.30% of the passwords, outperforming PCFG_v4. 1 by 56.10%. Additionally,for better password-composition guidelines, we find that long password-composition policies requiring more segments are more resistant to guessing attacks. For the segment, the password <italic>12zxcvbnword1997</italic> has four segments since it follows the template <inline-formula> <tex-math notation="LaTeX">${Digit}_{2}{Keyboard}_{6}{Letter}_{4}{Year}_{4}$ </tex-math></inline-formula>. We thus recommend users to create long passwords with four or more segments instead of the widely recommended more character classes for security.
A Novel Multi-Branch Channel Expansion Network for Garbage Image Classification

Due to the lack of data available for training, deep learning hardly performed well in the field of garbage image classification. We choose the TrashNet data set which is widely used in the field of garbage image classification, and try to overcome data deficiencies in this field by optimizing the network structure. In this article, it is found that the deeper network and short-circuit connection, which are generally accepted in the field of deep learning, will not work well on the TrashNet data set. By analyzing and modifying the network structure, we propose an effective method to improve the network performance on TrashNet data set. This method widens the network by expanding branches, and then uses add layers to realize the fusion of feature information. It can make full use of feature information at slight additional computational cost. Using this method to replace the core structure of the Xception network, the performance of the improved network has been improved greatly. Finally, the M-b Xception network proposed by us achieves 94.34% classification accuracy on the TrashNet data set, and has certain advantages over some state-of-the-art methods on multiple indicators. The python code can be download from https://github.com/scp19801980/Trash-classify-M_b-Xception.
Influence of Different Halbach Arrays on Performance of Permanent Magnet Synchronous Motors

Halbach array is a hot research topic for motor researchers in recent years. With the aid of finite element analysis method, the traditional Halbach, uneven thickness Halbach and soft magnetic Halbach are compared and studied under the same amount of permanent magnets. The air gap magnetic density, AC-DC axis inductance and reluctance torque of the three Halbach arrays are analyzed. The simulation results show that the traditional Halbach motor has the largest air gap magnetic density but does not have reluctance torque. The air gap magnetic density of Halbach motor with nonuniform thickness and unequal thickness is the smallest, but reluctance torque is generated due to unequal inductance of AC and DC axis. The motor with soft magnetic Halbach not only has larger air gap magnetic density, but also has larger reluctance torque.
Real-Time Characteristics of ROS 2.0 in Multiagent Robot Systems: An Empirical Study

Due to its ease of use and flexibility, the robot operating system (ROS) is increasingly becoming the most popular middleware for robot applications, even in multiagent systems. Since ROS 1.0 does not satisfy real-time requirements, ROS 2.0 was developed, and it improved the communication stack with the real-time data distribution service (DDS) protocol. However, the actual performance level to be expected is still unknown and can largely depend on the operating system and the kernel being used, the DDS distribution, and the overall software load of the system. In this article, we present an empirical study that evaluates the real-time performance of ROS 2.0 in both the system and communication software layers. In the system layer, the deterministic behavior of the ROS 2.0 nodes is thoroughly observed with regard to whether the tasks are schedulable and can function within the specified temporal deadline. In the communication layer, special attention is devoted to the rate of data loss and the overall latency of messages between nodes. Experiments are performed in various working conditions; for example, the system load is increased to define the real-time performance of the tasks. For reference, the results are compared with the those from the traditional ROS variation. Moreover, we implement a multiagent service robot system to verify the suitability of ROS 2.0 for real-world applications. Our results show that the application of ROS 2.0 is more suitable than that of ROS 1.0 in terms of real-time performance.
ECHAD: Embedding-Based Change Detection From Multivariate Time Series in Smart Grids

Smart grids are power grids where clients may actively participate in energy production, storage and distribution. Smart grid management raises several challenges, including the possible changes and evolutions in terms of energy consumption and production, that must be taken into account in order to properly regulate the energy distribution. In this context, machine learning methods can be fruitfully adopted to support the analysis and to predict the behavior of smart grids, by exploiting the large amount of streaming data generated by sensor networks. In this article, we propose a novel change detection method, called ECHAD (Embedding-based CHAnge Detection), that leverages embedding techniques, one-class learning, and a dynamic detection approach that incrementally updates the learned model to reflect the new data distribution. Our experiments show that ECHAD achieves optimal performances on synthetic data representing challenging scenarios. Moreover, a qualitative analysis of the results obtained on real data of a real power grid reveals the quality of the change detection of ECHAD. Specifically, a comparison with state-of-the-art approaches shows the ability of ECHAD in identifying additional relevant changes, not detected by competitors, avoiding false positive detections.
DermaDL: Advanced Convolutional Neural Networks for Automated Melanoma Detection

Early detection of melanoma, one of the deadliest types of cancer, is of paramount importance. Currently, the use of Convolutional Neural Networks (CNNs) is the main line of investigation for the automated detection of this kind of disease. Most of the existing works, however, were designed based on transferlearning general-purpose architectures to the domain of skin lesions, posing inflexibility and high processing costs to the task. In this work, we introduce a novel architecture that benefits from cutting-edge CNNs techniques Aggregated Transformations combined to the mechanism of Squeeze-and-Excite organized in a residual block; our architecture is designed and trained from scratch having the melanoma problem as goal. Our results demonstrate that such an architecture is competitive to major state-of-the-art architectures adapted to the melanoma detection problem. Having a fraction of the number of weights of previous works, our architecture is prone to evolve and to provide low processing cost for real-world in situ applications.
Waste Profiling and Analysis using Machine Learning

A large amount of solid waste is generated in the urban areas, and these wastes contain different types of substances like organic wastes, paper, plastic, metal, glass, etc. which needs to be treated separately for efficient waste management. This indicates that the wastes which are dumped all together need to be separated into categories before treating them. To support this process, Government introduced the concept of wet and dry wastes for civilians to dump wastes accordingly. If these norms are followed strictly, a huge amount of budget for waste segregation will be saved, which can be used for further waste treatment. Keeping this in view, this paper has proposed the system which can classify the waste as dry waste or wet waste based solely on the image of the waste taken. Focusing on simplicity, it is intended to propose an application which will only be required by the civic bodies to upload the captured images of garbage bins and sent to the system to analyze whether the garbage is wet, dry or mixed. The detection of contents of the garbage is the crucial aspect which will be done using machine learning. This idea can contribute in the near future to help analyze the waste disposal habits of people in different locations. This analysis can then be used to create awareness in required locations and help improve the waste disposal habits.
Electric Power Substation Reliability Assessment for Light Railway DC Traction System

The main objective of this paper is to present a reliability evaluation method for the electric power substation of the Light Rail DC Traction Electrification System. The Light Rail Systems which is a special part of rail systems will be introduced, power traction of Light Rail Systems (LRS) will be explained and the reliability analysis of power traction in LRS will be performed. In this study, the structural reliability model of the power substation is developed on physical-based modeling of the components and system configuration. This study only concentrates on determining the reliability of a substation, not including system-wide effects. The reliability analysis is carried out individually for each unit that is composing the system, and then the total reliability of the system is determined according to various scenarios. The reliability analysis of power supply to the traction is performed for a real system named Habipler- Topkapi LRS which is located in İstanbul, Turkey.
A Short Survey: Applications of Artificial Intelligence in Massive MIMO

The current mobile infrastructure faces an exponential growth in mobile traffic volumes which causes a demand on high data rate and low latency. Hence massive MIMO is one of the most promising technology of fifth Generation (5G) wireless communication systems due to its high data rate and system throughput. In this technology the major challenges are high computational complexity and great difficulties in the exploitation of the multiple antenna systems. Hence, this survey paper focuses on the issues on the Massive MIMO system in 5G wireless systems and the use of intelligence to resolve the challenges to improve the user experience and to utilize the radio resources in an effective way. Therefore, this paper provides a short survey that provides an overview of the implementation Artificial Intelligence in 5G wireless systems for solving various challenges in the Massive MIMO system.
Tunable Dielectrophoretic Traps for Extra-Cellular Vesicles*

Here we demonstrate nano-scale liposome templating using dielectrophoretic (DEP) force. When an AC electric field is applied between two parallel electrodes, the resulting DEP force will trap and physically confine the targets, allowing for dynamic manipulation without the need for mechanical components or hydrodynamic force. Moreover, due to the frequency dependence of the DEP effect, the physical confinement is adjustable and can be reversed by tuning the frequency. We believe that this approach will be useful for single Extracellular Vesicle confinement and analysis.
Genetic variability and performance of ginger genotypes under mid-hills of Meghalaya

Twenty-one indigenous and exotic genotypes of ginger were evaluated for three consecutive years. All the 12 parameters were significantly different among genotypes. Number of leaves per clump, weight of mother rhizome and internodal distance of rhizome were found to be the most variable characters (21.3–32.9% CV). Yield was positively and significantly correlated with tillers/clump (r=0.83), internodal distance of rhizome (r=0.51 and plant height (r=0.50) and was negatively correlated with fibre content (r=−0.53). Karakai, Chekeralla, Rio-de-Janeiro, Thingpui and Khonsa Local had high rhizome curing percentage (19.1–20.4). The maximum fibre content (7.6%) was recorded in Khasi Local and lowest in Nadia. However, Tura yielded highest (266.9 q/ha) followed by Poona (250.4 q/ha) and Basar Local (248.8 q/ha).
Link Prediction using Influencer nodes of a Social Network

Social networks have grown exponentially in recent times. Hence, the analysis of social networks has proven to be very useful and has drawn many researchers into this research domain. In particular, link prediction remains as an important problem of social networks. Link Prediction has wide range of applications, where it is used for recommending friends on social networks such as Facebook and also in recommending products to customers in E commerce platforms like Amazon. This paper has proposed a new algorithm for link prediction by incorporating a centrality measure. This algorithm has been applied to real time social networks and it has performed better than the existing baseline methods.
A Review on Driver Distraction Detection Methods

One of the most relevant reason for death is road crashes. Road crashes may occur for many reasons. One of the causes is distracted driving. Distracted driving is the deviation of the driver’s consciousness from driving tasks due to some secondary tasks like eating talking etc. It may cause death, injuries and economic losses. The alert given to the driver on time can avoid most of these problems and improve transportation safety. However, the development of such systems can solve many difficulties related to fast and proper recognition of a driver’s behavior. Different methods have been used to determine the forms of driver disturbances and to explain the consequences in terms of driving performance and participation in crashes. There are three different types of distractions present, visual distraction, manual distraction, and cognitive distraction. Head, eye movements are one of the strong indicators of driver’s distraction. We know remote eye tracking has emerged recently, which gives an opportunity of real-time identification of visual distraction. In this paper discussed visual along with the manual distractions.
Performance Comparison of Machine Learning Classifiers for Fake News Detection

Information sharing on the web particularly via web-based networking media is increasing. Ability to identify, evaluate and address such information is significantly important. Fake information deliberately created is purposefully or unintentionally engendered over the internet. This is affecting a larger group of society who are blinded by technology. This paper illustrates model and methodology to detect fake news from news article with the assistance of Machine learning and Natural language processing. In this proposed work different feature engineering methods like count vector, TF-IDF and word embedding are used to generate feature vector. Seven different Machine learning Classification algorithms are trained to classify news as fake or real and are compared considering accuracy, F1 Score, recall, precision and best one is selected to build a model to classify news as fake or real.
Diverse Feature Blend Based on Filter-Bank Common Spatial Pattern and Brain Functional Connectivity for Multiple Motor Imagery Detection

Motor imagery (MI) based brain-computer interface (BCI) is a research hotspot and has attracted lots of attention. Within this research topic, multiple MI classification is a challenge due to the difficulties caused by time-varying spatial features across different individuals. To deal with this challenge, we tried to fuse brain functional connectivity (BFC) and one-versus-the-rest filter-bank common spatial pattern (OVR-FBCSP) to improve the robustness of classification. The BFC features were extracted by phase locking value (PLV), representing the brain inter-regional interactions relevant to the MI, whilst the OVR-FBCSP is used to extract the spatial-frequency features related to the MI. These diverse features were then fed into a multi-kernel relevance vector machine (MK-RVM). The dataset with three motor imagery tasks (left hand MI, right hand MI, and feet MI) was used to assess the proposed method. Experimental results not only showed that the cascade structure of diverse feature fusion and MK-RVM achieved satisfactory classification performance (average accuracy: 83.81%, average kappa: 0.76), but also demonstrated that BFC plays a supplementary role in the MI classification. Moreover, the proposed method has a potential to be integrated into multiple MI online detection owing to the advantage of strong time-efficiency of RVM.
EXPLORATION OF MICROORGANISMS AS A POTENTIAL SOURCE OF XANTHINE OXIDASE INHIBITORS: AN UPDATED REVIEW

Nowadays the prevalence of hyperuricemia has significantly increased in which serum uric acid levels are exceeding the normal range. Gout is the predominant clinical implication of the hyperuricemia, but many clinical investigations have confirmed that hyperuricemia is an independent risk factor for cardiovascular disease (CVD), hypertension, diabetes, and many other diseases. The xanthine oxidase (XO) converts hypoxanthine to xanthine and ultimately to uric acid, and the irreversibly accumulated uric acid causes hyperuricemia associated with gout. Hence specific and selective xanthine oxidase inhibitors (XOI) are potentially powerful tools for inactivating target XO in the pathogenic process of hyperuricemia (Gout). The objective of the current study was to overview the various XOI isolated from the microorganisms. Microorganisms have been employed for several decades for the large-scale production of a variety of bio-chemicals ranging from alcohol to antibiotics and as well as enzyme inhibitors. Currently available XOI (allopurinol and febuxostat) for the treatment of gout have been exhibiting serious side effects. Thus, there is a need to search for new molecules to treat hyperuricemia and its associated disorders. At present, microbes have been unexplored in the development of successful products for the management of XO-related diseases. Hence, the present review focused on novel XOI produced from various microbial species such as Actinobacteria, lichens, bacteria, endophytic fungi and mushrooms, which can be expected to play an important role in the ongoing transition from the empirical screening to the real rational drug design.
Processing of EEG Signal for Classification of Epilepsy

This paper aims at analysing the electroencephalogram of the local, epileptic population of Chennai. The subjects used for this study suffer from different types of epilepsy. Their EEG was recorded at a sampling rate of 256 Hz. 10 second durations of these signals were exported and preprocessed using a moving average filter to remove the noise, and filtered with a band pass filter to pass the required frequency range. Wavelet denoising using Daubechies wavelet removed the artifacts in the signal, and the denoised signal was used for feature extraction. The features used are the Hjorth parameters, which characterize the power and frequency of the signal. These parameters were estimated for pre-interictal, interictal and post-interictal states. The correlation between the Hjorth parameters were plotted for all the patients, electrode wise, in order to understand the dominant electrode for the population sampled, in case of an epileptic event. The correlation proved that general epilepsy was the most common type of epilepsy existing in the sample population. It was inferred after calculating the p-values, that the Hjorth parameters characterize the epileptic signal excellently.
Demand based Variable Electricity Tariff Meter

Basic economics dictates that the consumption pattern based on supply demand curve is most efficient. In current electricity billing system prevalent in India people who are consuming electricity for essential purpose and those who are consuming for luxurious purpose are paying equally until they cross a monthly threshold. Through this paper, we introduce Demand Based Variable Electricity Tariff Meter with a circuitry designed to tackle this problem. This meter measures the amount of units of power consumed on hourly basis. The tariff rate varies for different time slots in the day. A threshold limit of usage for every time slot is calculated based on minimum electricity required to run essential appliances. The tariff rate increases when this limit is reached thus a consumer who uses luxurious appliances will exceed this limit and thus will pay higher tariff for usage beyond the limit. In this paper billing amount is calculated with this tariff system and is compared with that of electricity regulators in Maharashtra such as Maharashtra State Electricity Board (MSEB) and Brihanmumbai Electric Supply (BES). A reduction in charges for consumers using electricity for essential purposes and also those using it in off peak time slots is observed.