Miracle-dz/UniFAR-FTUs huggingface dataset:
Load Forecasting Method Based on CS-DBN-LSTM

Accurate load forecasting can improve the take-up rate of electricity, and ensure the electricity demand of residents for production and living can be met in time. A load prediction method integrated DBN and LSTM was adopted in the paper. Model used historical load data and weather data as data to improve prediction accuracy, since electricity is affected by factors mentioned above. The cuckoo search optimization was also introduced to find the best parameters for improving the prediction accuracy. The experiment result showed that the load forecasting algorithm proposed is with higher accuracy for the load forecasting compared with DBN, LSTM and DBN-LSTM.
Fairness in Sensor Detection Systems

Considering a sensor system to detect the occurrence of an emitter in multiple communities, this paper discusses the issue of fairness in such systems. Fairness can be an issue because sensor detection systems can be configured in multiple ways and the resulting detection performance can vary from community to community. In this paper, we argue for a framework based on max-min envy-free fairness; and we show that there are conditions when it is possible to design distributed detection systems that are max-min envy-free fair. We discuss the loss of detection power in a fair system and we propose a procedure to allocate sensors to reduce the loss of detection power and design a max-min envy-free fair system.
Towards a secure Software Defined Network with Adaptive Mitigation of DDoS attacks by Machine Learning Approaches

DDoS attacks produce a lot of traffic on the network. DDoS attacks may be fought in a novel method thanks to the rise of Software Defined Networking (SDN). DDoS detection and data gathering may lead to larger system load utilization among SDN as well as systems, much expense of SDN, slow reaction period to DDoS if they are conducted at regular intervals. Using the Identification Retrieval algorithm, we offer a new DDoS detection framework for detecting resource scarcity type DDoS attacks. In designed to check low-density DDoS attacks, we employ a combination of network traffic characteristics. The KSVD technique is used to generate a dictionary of network traffic parameters. In addition to providing legitimate and attack traffic models for dictionary construction, the suggested technique may be used to network traffic as well. Matching Pursuit and Wavelet-based DDoS detection algorithms are also implemented and compared using two separate data sets. Despite the difficulties in identifying LR-DoS attacks, the results of the study show that our technique has a detection accuracy of 89%. DDoS attacks are explained for each type of DDoS, and how SDN weaknesses may be exploited. We conclude that machine learning-based DDoS detection mechanisms and cutoff point DDoS detection techniques are the two most prevalent methods used to identify DDoS attacks in SDN. More significantly, the generational process, benefits, and limitations of each DDoS detection system are explained. This is the case in our testing environment, where the intrusion detection system (IDS) is able to block all previously identified threats
An Improved Non-Destructive Shrimp Freshness Detection Method Based on Hybrid CNN and SVM with GAN Augmentation

In global seafood industries, production and consumption of Indian white shrimps (Vannamei) have considerably increased due to its sense of taste and nutritive value. At the same time, monitoring its perishable quality and shelf life to meet export standards is proving to be a major concern and also preserving the shrimps is very hard. The freshness of shrimp is one of the important qualities that consumers care about because deteriorated shrimp have many spoilage microorganisms which are harmful to human well-known. Deep learning technology exactly tackles the problem of selecting the features manually. However, the deep learning technology requires a large number of training samples. To address this, we adopted Generative Adversarial Network (GAN) for data augmentation to enhance the original training set. We proposed a hybrid CNN and SVM model with application of GAN based augmentation and investigated with traditional method which was also trained under different setting for extensive comparison study. The qualitative results show that model trained under GAN augmentation shows better identity. Lastly, we conclude that the hybrid CNN and SVM presents lower complexity with higher accuracy of 98.1%, suitable for real time and automated technique in industrial-level applications.
Analysis of Short-Circuit Characteristics and the Design of a Novel Protection Circuit for SiC MOSFETs

The short-circuit capability is one of the important issues that are related to the safe operation of silicon carbide (SiC) MOSFETs. The short-circuit mechanism of SiC MOSFET is analyzed, and the impact of driving circuit parameters on short-circuit characteristics is presented. Compared to IGBT modules, SiC modules have higher di/dt and weaker ruggedness, so the conventional desaturation protection circuit cannot be directly used for SiC modules due to its relatively slow response. Thus we put forward a novel desaturation protection circuit, and present the comparison between the conventional protection circuit and the novel one. At last, short-circuit protection tests under typical hard switching fault and fault under load was conducted to verify the effectiveness of the novel desaturation protection circuit. The experimental results show that the novel desaturation protection method has a faster protection speed than the conventional one, and can ensure the reliable operation of SiC MOSFET power modules.
Enhanced Framework for Active Prosthetic Arm

In medication, prosthesis is an imitation of a body part lost due to injury, infection, or innate conditions. An individual's prosthetics must be planned and collected by the patient's appearance and practical requirements. Recently, prosthetics have evolved from passive prosthetics which primarily fulfilled aesthetic requirement to an active prosthetic which can be controlled by body signals. Several, microcontrollers based prosthetic designs have been presented by several researchers. Recently few electromyogram (EMG) controlled prosthetic designs have also been proposed. With advancements in artificial intelligence, the active prosthetic paradigm has been elevated. Use of artificial intelligence and deep learning has shown results with higher degree of control and accuracy. In this paper, we present a survey of current technologies and progresses for designing prosthetics for physically disabled. The paper discusses about the key features and challenges of the various designs. Further, an easy design with more degrees of freedom for prosthetic has been presented.
Min-Max Optimal Control for High-Speed Train Movements with Exogenous Disturbances and Uncertain Parameters

This study investigates the min-max optimal control of high-speed train movements considering worst-case exogenous disturbances and uncertain parameters simultaneously. For a high-speed train, each car is modeled as a point mass connected by flexible coupling to track the reference speed profile of the train. Based on the robust model predictive control (RMPC) approach, a min-max optimal control problem is formulated to reduce the energy consumption and tracking errors while satisfying some individual constraints. The desired control inputs can be obtained by solving the min-max optimization problem.
Starting improvement of micro-wind turbines operating in low wind speed regions

ABSTRACT The study deals with the design and optimization of external and internal geometry of micro-wind turbines blades. A specified objective function which consists of the power coefficient and the starting time was defined and the genetic algorithm optimization technique in conjunction with the blade-element momentum theory was adopted to find the geometry of the blades including the distributions of the chord, the twist angle and also the shell thickness. Moreover, the allowable stress of the blades was considered as a constraint to the objective function. Results show that a reasonable compromise is achievable such that the starting time of the blades reduces noticeably in return for a small drop in the power coefficient. The significant improvement of the hollow blades over the solid ones indicates that the power coefficient and the starting performance could be improved through the appropriate distributions of the considered decision variables, i.e. the chord, the twist angle and also the shell thickness.
Comparative Analysis of Direct and Indirect Current Control of Master-Slave Scheme for Microgrid

The master-slave control is an important scheme to control the microgrid in both islanded and grid-connected modes. It has been proved that it is competitive to the droop control schemes. This presented paper shows two techniques to control the current/power of the slave inverters to precisely adjust the injected output power of the slave inverters. The adopted microgrid contains two multilevel (five-level diode-clamped) inverters. One inverter is operated as a slave inverter and the remaining inverter is operated as a master inverter. The slave inverter can be operated using two techniques. The slave inverter is operated in a direct current control (DCC) mode using nested multiband hysteresis current control. While the second technique is the indirect current control (ICC) mode through the cascaded structure of power and current loops. Simulation results show a comparative analysis, which affirms that the DCC gives much better performance for current/power control, while the ICC gives a slightly better performance for voltage stabilization at loads.
Heart Sound Classification based on Fractional Fourier Transformation Entropy

Automatic classification of heart sounds has been studied for many years, because computer-aided auscultation of heart sounds can help doctors make a preliminary diagnosis. We propose a classification method for heart sounds that uses fractional Fourier transformation entropy (FRFE) as the features and a support vector machine (SVM) as the classification model. The process of the whole method is cutting of heart sounds, feature extraction, and classification. We compare FRFE of different signal orders, and finally evaluate fused features of multiple orders according to the better classification results. These fused features are used as the input of a SVM, a k-nearest neighbour (KNN), and a Naıve bayes classifier (NBC) to compare the most suitable classifiers. Finally, we consider the fused features that reflect both the time and the frequency domain to achieve a better classification performance.
Network Intrusion Detection using Decision Tree and Random Forest

With the growth in technology and increase in the usage of the same, we have seen that how Network Intrusion Detection has emerged and have also became a most important aspect in the area of research. The Intrusion Detection System broadly classifies the attack as normal or hostile attack based on the activities of the users. An Intrusion Detection System deals with Traffic data of networks. It is non-linear and can deal with complicated problems. In the past researches, many Intrusion Detection system was proposed with different accuracy levels. Even though there is no model that can precisely detect or predict an attack. Therefore, it is important to develop a robust and effective Intrusion Detection Model. In this paper a Network Intrusion Detection System is developed using Decision Tree and Random Forest classifier. These techniques give us a better accuracy and performs pretty well when compared with some other traditional classifier for classifications of attack effectively. The NSL-KDD dataset have been used to conduct our experiments and evaluate the performance of our model. The final results shows that our proposed method is efficient enough to give a low false alarm rate and high detection rate.
Presenting Multiple Viewpoints Inspired by Art

The paper presents an innovative method to artfully present multiple human and nonhuman viewpoints on a user’s current viewpoint. The method is built on a digital platform for capturing a large number of human and nonhuman viewpoints in the world, and enabling us to collectively share the viewpoints. Inspired by the art, the presentation method enables people to watch the world in a novel way.
Analysis and Characterization of Mental Health Conditions based on User Content on Social Media

Social networking media is presently utilized to understand the mental health of individuals as well as any health-related issues. Computer science researchers are currently applying statistical methods for the prediction of specific mental health disorders. Several studies have investigated to predict symptomatology like suicidality, depression, and anxiety. These research studies provide great benefits for diagnosing, monitoring, and designing intervention plans for people suffering from any mental health status. This paper conducts a systematic review of the studies predicting mental health conditions using social networking information. We analyzed several aspects of the existing studies such as design methods and strategies. This paper investigated the various Natural Language Processing strategies and Deep Learning methods used to detect mental health status
Evaluation of feature extraction methods with ensemble learning for breast cancer classification

A breast cancer is known as one of the most dangerous disease of the death causes among aged 40-55 women. We need to develop a computer aided diagnosis system for breast cancer classification. In the previous study, the random forest of the ensemble learning method was reported to be one of promising classifiers for classifying breast cancers using a Wisconsin Diagnostic Breast Cancer(WDBC) dataset. Furthermore, we found the effectiveness of the random forest with recursive feature elimination(RFE) for breast cancer classification, compared to another ensemble learning methods, XGBoost and LightGBM(LGBM). The RFE is known as a feature selection method. In our experiments, however, there was no comparison with the feature extraction of principal component analysis(PCA). This paper presents the evaluation of feature extraction methods with ensemble learning for breast cancer classification on the WDBC dataset.
Content Analysis of Chinese Netizens' Public Opinion Based on Internet Comments: Case study of 160,000+ comments from Guan Video - a social political channel followed by four million people

When a social event is reported, public opinions that an individual could access are predominantly limited by mainstream newspapers, social media, and how they portray the voice of their audiences. Recommendation algorithms from those platforms could be biased based on those channels' political standpoints. Therefore, the comments under news sections could be manipulated and not serve as an optimal proxy for reflecting public opinions. Social groups with different interests and standpoints are constantly conflicting with others. The measurement of social opinion can help us study the social demands behind public opinion and the social conflicts that come with the demands. Thus, content analysis of comments on Internet news articles proves effective in quantifying and understanding the different demands and conflicts in society more accurately, making it possible to govern a more developed and complex society. Carrying the goal to visualize abstract and subjective social opinions into measurable data, and to prove the effectiveness of applying data in assisting social studies, this research analyzes 160,000+ internet comments from Guan Video, a social-political channel that is followed by four million people through the Chinese video platform Bilibili, which has over 223 million Chinese users. With machine learnings IF-IDF model and Word2Vec model, this research proves the effectiveness of applying innovative quantitative measures in social research through the case study of netizens' opinions on education topics. Furthermore, we hope this research inspires further quantitative studies in humanity and society with the assistance of Natural Language Processing and other technologies.
Principal Axes for Seakeeping Response Processes

This paper complements an earlier paper by the same author titled Joint Seakeeping Response Processes for Determining Structural Loads, presented at the 2002 SNAME Annual Meeting. That paper put forward a variety of methods appropriate to the characterization of joint processes, including phase cofactors, cross cospectral moments, and joint normal techniques. The present paper exploits cross cospectral moments to determine principal axes for joint seakeeping processes and explores their behavior and properties in appropriate parameter spaces. The convergence of time domain realizations of these joint processes on the principal axes is also demonstrated through Fourier-Stieltjes simulations and conditional probabilities. One example application is to determine the orientation in space of principal axes for vector processes, such as translational accelerations in vessel coordinates. As shown by a referenced paper cited in the conclusions, the method can be extended to Von Mises stress and rainflow fatigue.
Energy Efficiency Optimization for Cooperative SWIPT Network Based on Continuously Adjustable Power Splitter

With the continuous improvement of wireless communication technology, expanding wireless communication infrastructures consume more energy than ever before. This situation raises the need to improve energy efficiency. This paper considers system energy efficiency optimization with SWIPT technology in cooperative wireless network. And as the value of power split ratio affects the energy efficiency of the transmission network, a continuously adjustable power splitter in the SWIPT receiver is designed. Based on the continuous power distribution coefficient ρ, we give a cooperative SWIPT transmission network model and a transmission energy efficiency optimization problem. Compared with the traditional SWIPT transmission scheme based on discrete power splitter, our method is more practical for engineering. Our simulation results demonstrate that our method can improve the energy efficiency of the transmission network.
Regulatory networks of circRNAs associated with protein modification in the morphogenesis of Populus euphratica Oliv. leaf shape

Noncoding RNAs and protein post-translational modifications (PTM) play an important role in plant development. To investigate the role of PTM-associated circRNAs in the morphogenesis of Populus euphratica Oliver heteromorphic leaves, we analyzed four heteromorphic leaves of P. euphratica using miRNA-seq and RNA-seq. According to the sequencing results and the ceRNA hypothesis, the circRNA-miRNA-mRNA regulatory relationships existing in the morphogenesis P. euphratica heteromorphic leaves were established. Then they were enriched GO analysis based on the function of the targets of circRNAs. It was found that 17 circRNAs modulated PTM by antagonizing 51 miRNAs in P. euphratica, and these RNAs could co-regulate the occurrence of P. euphratica heteromorphic leaves through mainly protein phosphorylation modification and protein panning regulating.
A Link Prediction Algorithm Combining Node Influence and New Biased Random Walk

Recent years have seen a lot of interest in link prediction. Essentially, it means designing a prediction algorithm that is capable of accurately describing a certain network mechanism in order to get more accurate predictions. In complex network research, it has important applications. The DeepWalk method randomly samples neighbor nodes, and it does not fully consider the node position itself, so insufficient information considered in node sequence sequence sampling When using DeepWalk for link prediction, the node's own influence characteristics are not considered. First of all,we change DeepWalk through biased random walk, learns the vector representation of nodes, and then fuses the influence between node pairs and the similarity between node pairs to raise the accuracy of link prediction. We have experimented the algorithm of this paper on real network data, and from the experimental results, we can see that the algorithm of this paper works better than other link prediction algorithms.
Graph Convolution Neural Network-based Revelation of Students' Career Expectations

Clarifying the career expectations of freshmen is an important means to improve their future employment satisfaction. In the past, colleges and universities usually used questionnaires or personal interviews to understand students' career expectations. However, due to the complexity of everyone's goals and ideas, it is difficult to accurately predict students' professional expectations, and it is time-consuming and labor-intensive. With the development of data mining technology, many scholars use student behavior data to predict students' career expectations, but a large amount of data is needed for model training. Recent studies suggests that we can use the Career Planning Book assignment submitted by students to mine the career expectations of students. However, the career planning education of students in most domestic universities has not been carried out for a long time, and it is difficult to obtain a large amount of text data. Traditional data mining techniques, such as SVM and KNN, are not suitable for such small sample data, while Graph Convolutional Neural Network (GCN)model has a high accuracy for small sample data. Therefore, this paper proposes a model based on GCN, which can mine students' career expectations through Student's Career Planning Books. The experimental results show that GCN model can mine students' career aspiration with high accuracy under a circumstance of less data.
Current Characteristic Values Estimation for Losses Calculation of Inductor in Dual Active Bridge

During the design of the dual active bridge (DAB), accurately calculating the losses and the temperature rise of the inductor in it is very important for the reliable operation of the whole circuit which is built by wide band-gap semiconductors. This paper presents a general method for accurate and convenient inductor current characteristic values estimation based on DAB operating characteristics. This paper gives a model for calculating the core loss and winding loss of the inductor based on these characteristic values and gives the temperature rise caused by losses. The commutation process and resonance process during DAB operation are analyzed, and two kinds of magnetic flux density loops of the inductor in DAB are given. An experimental platform with DAB based on SiC MOSFET was built. The experimental results show that the errors between the estimated values calculated by the given methods and the measured values are less than 4%.