Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Physical Field Optimization Analysis of High-Speed Permanent Magnet Synchronous Motor Based on NSGA-II Algorithm

In this paper, a multi-objective optimization design method based on Nondominated Sorting in Genetic Algorithm (NSGA) for high-speed permanent magnet synchronous machine (HSPMSM) is proposed to realize high power density and high efficiency. This method takes into account both the rotor strength, the permanent magnet temperature problems and avoids the tedious iterative process of high-speed motor design. In this paper, this method is used to optimize a 75 kW, 60 000rpm high-speed motor. Taking the efficiency and power density of the motor as the optimization goal. Then, sensitivity analysis of parameters affecting optimization objectives will be done. Finally, after optimization, the efficiency reached 98.44% and the power density reached 6.94 kW/kg.
Real-Time Separation of Collided Signals in Multiple Zones Backscatter Communication System

Backscatter communication is essential in applications where wireless and batteryless sensors are demanded. Backscatter communication system may suffer short communication range because its sensors transmit signals by means of the reflection of incident radio wave that is provided by counterpart interrogator. The use of multiple interrogators, forming multiple interrogation zones, is the usual practice to extend the range of backscatter communication system. However, if a sensor receives more than one radio waves — one from its counterpart interrogator and others from neighbor interrogators — the sensor produces modulated backscatters to all the interrogators, causing interference to neighbor interrogators. This inter-zone interference is particularly problematic when multiple sensors send stream data concurrently. This paper proposes a real-time separation of collided backscatter signals based on their statistical independence. The proposal uses a priori probability of backscatter signal to extract the independent carrier phase angles and the independent amplitudes from IQ signal observation. The measurement of channel state information with predefined symbol pattern in packet is not needed. The proposal is evaluated both in numerical simulations and a physical experiment using prototype backscatter sensors and a software-defined interrogator. The results show that collided backscatter signals can be separated in real-time without degrading the packet error rate of each signal.
The Effect of Website Components on User Trust in Increasing the Interest to Use Public Administration Service on E-Government Website

The use of E-Government services, especially in Indonesia, is still not optimal. This is evidenced by the results of a survey conducted by the E-Government Development Index (EGDI) in 2018, Indonesia is ranked 107 out of 193 countries. In the Lower Middle Income Asia category, Indonesia lags behind Vietnam, India, the Philippines and Sri Lanka. One of the factors that influence the intention to use services is user trust. The purpose of this study is to analyze the components of the website on user confidence in e-government services and the intention to use services so that it is expected to increase the acceptance of e-government services in Indonesia. The study used a quantitative approach through a questionnaire filled out by 34 respondents. The results found that customer service and visual design components are the components that most influence the user’s trust in e-government services.
Chronic Stress Level Estimation Focused on Motion Pattern Changes Acquired from Seat Pressure Distribution

In modern society, chronic stress in the workplace is a serious problem because it causes numerous diseases. To prevent chronic stress, it is necessary to continuously measurement this type of stress in the workplace. Thus, it is necessary to realize a chronic stress estimation method with small load to users. Therefore, we proposed a method to estimate chronic stress level by measuring changes of body motion patterns during desk work caused by chronic stress. For the acquisition of body motion patterns we called "Motion Pattern", a cushion-type sensor device with six pressure sensors was developed, and the changes of seat pressure distribution during desk work were measured. "Motion Pattern" was the time-series pattern of four"Motion Labels" including "Impact Label" which represented the motion that impacts the seat. We tried to estimate chronic stress level by learning the relationship between this "Motion Pattern" and the score of stress evaluation questionnaire. As a result, it was found that there was a significant difference in the interval time of "Impact Label" occurrence in all six participants. In addition, we achieved to estimate the existence and non-existence of chronic stress with an average accuracy of 87.0% using a classifier learned for each individual and achieved to estimate chronic stress level with a maximum accuracy of 70.0%for one participant. It suggests that our proposed method has potential for chronic stress monitoring in the workplace.
Compression Techniques Used in Iot: A Comparitive Study

Due to the improvement of technology, most of the devices used nowadays are connected to the internet, therefore a huge amount of data is generated, transmitted, and used by these devices. In general, these devices are limited in resources such as memory, processors, and battery lifetime. Reducing the data size reduces the energy required to process this data, minimizes the storage of this, data and the energy required to transmit this data. The need for applying data compression techniques on these devices will come in handy. This paper provides a survey and a comparative study among most commonly used IoT compression techniques. The study addresses the techniques in terms of different attributes such as the compression type, lossless or lossy, the limitations of the compression technique, the location of where the compression is applied, and the implementation of the compression technique.
Effective Learning Algorithms for Search and Rescue Missions in Unknown Environments

During the last decade, there has been a huge attention on deployment of unmanned aerial vehicles (UAVs) for both civilian and military missions. Alike swarming phenomena in animals and insects, these herds of smart robots could perform complicated task when working together. However, doing so needs smart coordination between individual subsystems. In this paper, aerial coordination by a UAV (also known as aerial shepherd) for a group of unmanned ground vehicles (UGVs) is investigated. The problem is separated in two sections. In the first stage, the aerial leader learns how to find a target using both Q-Learning and DQN techniques. In the next step, the optimal path is sent through a communication link to a group of mobile robots for ground coordination. Through simulations, we study the problem in a model-free environment with existence of stochasticity in control input and the environment. Once the target has been localized, the optimal path is sent to ground mobile robots to provide rescue. The obtained results for a primitive environment indicate successful coordination of mobile robots on the ground.
Implementation of a Quality Measurement Software for Virtual Reality Content on a head mount display

In a VR streaming content, an application software is necessary to control both a content quality on a streaming service and a display quality on a head mount display (HMD) according to loads for head mount display, computer facilities and network considering a user’s requests. In this paper, we focus about a quality measurement software for Virtual Reality (VR) content to control display quality on a HMD and discussabout its implementation.
Can a Conversation Paint a Picture? Mining Requirements In Software Forums

The modern software landscape is highly competitive. Software companies need to quickly fix reported bugs and release requested new features, or they risk negative reviews and reduced market share. The amount of online user feedback prevents manual analysis. Past research has investigated automated requirement mining techniques on online platforms like App Stores and Twitter, but online product forums have not been studied. In this paper, we show that online product forums are a rich source of user feedback that may be used to elicit product requirements. The information contained in forum questions is different from what has been described in the related work on App Stores or Twitter. Users often provide detailed context to specific problems they encounter with a software product and other users respond with workarounds or to confirm the problem. Through the analysis of two large forums, we identify 18 different types of information (classifications) contained in forums that can be relevant to maintenance and evolution tasks. We show that a state-of-the-art App Store tool is unable to accurately classify forum data, which may be due to the differences in content. Thus, specific techniques are likely needed to mine requirements from product forums. In an exploratory study, we developed classifiers with forum specific features. Promising results are achieved for all classifiers with f-measure scores ranging from 70.3% to 89.8%.
Quantum Neural Networks Learning Algorithm Based on a Global Search

This paper reports the performance of a novel training algorithm for quantum neural networks (QNN) using a variation of the quantum search algorithm. The proposed algorithm trains a QNN exploring all possible weights with a sublinear cost. The training cost is theoretically O(√N/t), as a function of the quantity N of possible weights and t is the number of possible solutions. Initial experimental results demonstrate that the algorithm always converges to existing solutions, in addition to having the mean and maximum values, almost in total, lower than the expected maximum amount, as theoretically expected. The training algorithm is applied to classification problems.
Auxiliary Snubber Cell for Dual BuckFull Bridge Inverter

In this paper, a novel zero-voltage transition (ZVT) snubber cell is proposed for dual buck full bridge inverter (DBFBI) under full-cycle and half-cycle modulation. Due to the characteristic of auxiliary snubber cell, the soft-switching condition can be realized within continuous conduction mode (CCM) and discontinuous conduction mode (DCM) of inductor current. The important feature of the proposed converter is that the efficiency of the system is improved through the resonant circuit while the high reliability of the converter is maintained. Moreover, the resonant circuit still be effective in two main modulation methods. The soft-switching can transfer the stored energy effectively and ensure the auxiliary circuit in a state of soft-switching. The zero-voltage-transition operation of the proposed converter is presented through theoretical analysis. The characteristics of the ASCDBFBI are verified with the 200k experimental prototype.
High-Density Power Conversion and Wide-Bandgap Semiconductor Power Electronics Switching Devices

Power electronics switching devices made on wide-bandgap (WBG) semiconductors are known to have the potential to make a transformative impact on 21st century energy economy. However, their market penetration has been slow primarily due to high cost and unknown application-level reliability. This article presents a comprehensive report on the history, current state of the art, and impending challenges in WBG power semiconductor technologies in order to break open this gridlock.
Parental influences on adolescents’ alcohol use

Alcohol has a complex role in Australian society. While most Australian adults drink alcohol at levels that cause few adverse effects, a substantial proportion drink at levels that increase their risk of alcohol-related harm (National Health and Medical Research Council [NHMRC], 2009). In many countries, including Australia, alcohol is responsible for a considerable burden of death, disease and injury; and alcohol-related harm is not limited to drinkers but also affects families, bystanders and the broader community (NHMRC, 2009; Room et al., 2010).
The Probabilistic Interval-Valued Hesitant Pythagorean Fuzzy Set and Its Application in Selecting Processes of Project Private Partner

The expression of fuzzy information under multi-attribute decision making (MADM) is constantly expanded by scholars to solve the problem of uncertain decision making in various application fields. Such as select project private partners, which is difficult to make an appropriate select in a complex and changeable environments. Although the aggregation operator under interval-valued hesitant Pythagorean fuzzy environment is an effective method to solve uncertain decision-making problems, there are still some drawbacks in aggregating operators that do not consider the information loss. In this paper, we develop Hamacher operations and Choquet integral-based method to solve select project private partner problem under probabilistic interval-valued hesitant Pythagorean fuzzy information, which could express decision-makers’ preference information more flexibly and consider the significance and the correlations among the elements. Firstly, we define the probabilistic interval-valued hesitant Pythagorean fuzzy set (PIVHPFS) as an extended mathematical expression of fuzzy sets (FS). Afterward, the Hamacher algorithm concepts are given under the PIVHPFS environment. Besides, we utilize Hamacher operations and Choquet interval-based method to develop the probabilistic interval-valued hesitant Pythagorean fuzzy Hamacher Choquet integral geometric (PIVHPFHCIG) operator. At the same time, some definitions and theorems based on PIVHPFHCIG operator are proposed. After that, we utilize the PIVHPFHCIG operator to develop an approach to solve the MADM problems under the PIVHPFS situation. The new method is feasible to overcome the drawback of information loss, and it is more reasonable for obtaining a better decision result. Finally, the introduction of the best project private partner selecting problem proves the effectiveness and feasibility of PIVHPFS, and the comparison between PIVHPFS and other similar techniques decision methods are also provided.
Economic Optimal Control of a U-loop Bioreactor using Simultaneous Collocation-based Approaches

In this paper, we consider economic optimal control of single-cell protein (SCP) production in a U-loop reactor. The model of the U-loop reactor contains both ordinary and partial differential equations. Consequently, the optimal control problems are large-scale. The optimal operating profile for the SCP production is an unstable attractor. Therefore, we consider two simultaneous collocation-based approaches for solving the optimal control problems. We implement these two approaches in C, and we use IPOPT to solve the involved nonlinear program (NLP). Finally, we present a performance study that demonstrates the feasibility of solving economic optimal control problems that involve the U-loop reactor in real-time.
A Mutual-Inductance-Based Impedance Model of Induction Cooker for Efficiency Improvement

Induction cooker i.e. IC, has huge market for high efficiency, reduced heating time and safety. IC technology develops towards higher induction efficiency and compatible to vessels with different material. These require an equivalent impedance model of IC, which is suitable for varying winding turns, frequency and vessel material. There were several impedance models of IC based on transformer model, however, without computational impedance characteristic. Hence, this paper proposed an impedance model based on mutual inductance. By analysis of eddy current field, the mutual inductance and equivalent impedance are derived. Based on that, the induction efficiency is analyzed, and effectively improved with optimal higher frequency and winding turns. Prototype with varying frequency, winding turns and material of vessel is built up, verifying the accuracy of this proposed impedance model. Furthermore, the induction efficiency is improved by 2.12% for GB45 vessel, and by 20.76% for Al vessel.
An Analysis on Use of Deep Learning and Lexical-Semantic Based Sentiment Analysis Method on Twitter Data to Understand the Demographic Trend of Telemedicine

Technology has turned into a fundamental piece of everybody's life. Social media technology is already used widely by the public to speak out once mind openly. This data can be leveraged to have a better understanding of the current state of decision making. However, Twitter data is highly unstructured. Sentiment analysis can be applied to such health-related data to extract useful information regarding public opinion. The aim of the research is to understand (i) the correlation between Deep Learning versus lexical and semantic-based sentiment prediction methods, (ii) the sentiment prediction accuracy of these methods on manually annotated sentiment dataset (iii) domain-specific knowledge on accuracy of the sentiment prediction methods, and (iv) to utilize Twitterbased sentiment to understand the influence of telemedicine in regards to heart attack and epilepsy. Four sentiment prediction methods are utilized for the research; Lexical and Semantic-based (Valence Aware Dictionary and Sentiment Reasoner (VADER) and TextBlob) and Deep Learning based (Long Short Term Memory (LSTM) and sentiment model from Stanford CoreNLP). The dataset that we retrieved consists of 1.84 million old health-related tweets. Our finding suggests that lexical and semantic-based methods for sentiment prediction offer better accuracy than Deep Learning methods; when a large enough and evenly distributed training dataset is not available. We observed that domain-specific knowledge affects the prediction accuracy of sentiment, mainly when the target text contains more domain-specific words. Sentiment prediction on Twitter data can be utilized to understand the demographic distribution of sentiment. In our case, we observed that telemedicine has a high number of positive sentiment. It is still in its infancy and has not spread to a broader demographic.
Evaluation of laparoscopic colposuspension and the tension-free vaginal tape procedure in the surgical treatment of female stress urinary incontinence

Though not a life threatening condition, involuntary loss of urine is a miserable situation. It has a multidimensional effect on the afflicted individuals, both men and women – and for the society. The purpose of this study was to evaluate two modern, minimally invasive surgical techniques for the treatment of female stress urinary incontinence (SUI). The techniques evaluated were laparoscopic colposuspension with mesh and staples (LCM) and the tension-free vaginal tape procedure (TVT). The study consisted of four parts. The first part (Study I) was an observational retrospective follow-up study. Data on the first forty patients operated on with LCM at Oulu University Hospital were collected. Patients had SUI or mixed urinary incontinence (MUI) with predominantly stress incontinence. The Studies II–IV were parts of a randomized, multicenter clinical trial, where LCM was compared with TVT. According to the predefined inclusion criteria 128 SUI women were randomly allocated into two treatment groups: 70 patients received TVT treatment as allocated and 51 LCM. There were seven drop-outs after randomization. After one year of follow-up the cure and improvement rate of the patients operated with LCM were ~ 90%. Also a significant improvement was found in Urinary Incontinence Severity Scores (UISS). At base line the score was 12.1 and after one year follow-up 2.7 (p<0.001). The bladder perforation rate was 15%. In Study II immediate cure rates and complications of LCM and TVT were studied. After six weeks of follow-up there was no difference in cure rates (~ 90%) between the procedures. There was no difference in complication rates. A significant difference was found in the use of anti-inflammatory / opioid drugs in the immediate post-operative period to relief the pain in favour for TVT. Hospital care was also significantly shorter after TVT than LCM. After one year of follow-up (Study III) TVT was found to give better result both objectively and subjectively. Negative stress test result was recorded in 85.7% in the TVT group and 56.9% in the LCM group. A significant difference was also found, when Visual Analoque Scale (VAS), King's College Health Questionnaire (KHQ) and UISS were used as outcome measures, in the favour of TVT. When 48-hour pad test was used as outcome measure there was no statistically significant difference between the groups. The cost-effectiveness (Study IV) of TVT was found to be better than that of LCM after one year of follow-up. In conclusion, the results of this study suggest, that TVT procedure is on the whole a cost-effective alternative for LCM in the treatment of female SUI.
A Genetic-Taguchi Global Design Optimization Strategy for Surface-Mounted PM Machine

This paper presents a novel step-optimization strategy for the multi-objective tradeoffs in surface-mounted permanent magnet synchronous machines (SPMSMs), i.e. genetic-Taguchi global optimization (GTGO). Specifically, the electromagnetic performances are accurately predicted by sub-domain (SD) model, and the objective functions of cost, efficiency and total harmonic distortion (THD) of back electromotive force (emf) are solved by genetic algorithm (GA) based on constraint conditions. Next, the Taguchi method optimizes local variables which are difficult to model by SD model, such as magnetic pole shift, magnet eccentricity and segmentation. The last, the effectiveness of proposed GTGO has been verified by finite-element analysis (FEA).
Split Ratio Optimization of Short-Time Duty PM Brushless DC Motors Considering Stator Winding Local Thermal Limitations

High-power-density short-time duty permanent magnet brushless DC motors (PM-BLDCM) has a large temperature gradient. There is a risk of insulation damage due to overheating of stator winding. Due to some application requirements, the dimensions, speed and torque of the motor are fixed. The split ratio and the maximum flux density of stator lamination (Bm), which are highly relevant to loss and temperature rise rate, are optimized to minimize the stator temperature. The current density limitation is considered in the optimization to avoid local overheating of stator winding. The analytical optimal combination of split ratio and Bm is obtained by the deduction of the expression of stator temperature and current density. The errors between analytical results and finite element analysis results are less than 8%, which proves that the optimization method can effectively limit the winding temperature.