Miracle-dz/UniFAR-FTUs huggingface dataset:
GAGPL: A Personalized Semantic Orientation Calculator in Dark Side Ternary Stars

We propose a new method called ”Dark Side Ternary Stars (DSTS)”, which is expected to allow emotional personal agents to interact with users. DSTS focuses on proper nouns to adapt preference to users. In this paper, we report only on ”GAGPL” in the DSTS. GAGPL is a method for calculating positive or negative value per sentence for an individual user. We aim to represent famous Japanese proverb ”The misery of others is as sweet as honey”. Our proposed method consists of Genetic Algorithms (GA) and Genetic Programming Like (GPL) algorithms. GA learns the positive or negative values of general word to individuals. GPL calculates the positive or negative value per sentence. We applied this method to the news article text data in Japanese professional baseball. As the experiment results, it shows that this method is effective.
An Analog Active Gate Drive Circuit Architecture for Wide Band Gap Devices

With the increased usage of SiC and GaN devices in power converters, it is apparent to engineers that the benefits of these devices for low switching losses have to be balanced against two deterrents: (1) the faster rise and fall times of these devices lead to increased harmonic content, making it more difficult to meet Electromagnetic Compatibility (EMC) regulations; and (2) increased sensitivity to parasitic elements in the board layout, leading to higher dvDS/dt and diD/dt with higher current and voltage peak stresses. "Active" gate drives are thus needed to regulate different regions of switching to achieve the optimal tradeoff between switching loss and EMC. We present a simple analog circuit architecture for an active gate drive. Turn on and turn off tests with SiC modules and discrete parts are discussed with experimental results.
An SLA-Aware Network Function Selection Algorithm for SFCs

5G is expected to support diverse services with different service requirements. Network Slicing (NS) is a new paradigm which aims to assign different services to a logically dedicated network. A network slice can host multiple services of similar requirements. The traffic flows belonging to a service are processed by a sequence of Virtual Network Functions (VNFs) which form a Service Function Chain (SFC). To utilize the resources efficiently, an operator can share the same VNF instance for multiple SFCs. The CPU utilization of a VNF instance can increase when new flows are accepted. This may increase the VNF delay which can result in the violation of SLA requirements of the existing flows. Therefore, an efficient VNF selection to form SFCs while meeting the SLA requirements is an important aspect that needs to be addressed. In this paper, we address the VNF selection problem for an SFC with the goal of supporting more number of SFCs by considering the dynamic variation of VNF delay. We develop a heuristic algorithm namely PENDANT which considers the effect of CPU utilization on VNF delay and migrates flows to other VNF instances to avoid SLA violations.
A Study on DC-Link-Type Quasi-Switched-Boost Inverters with Improved Voltage Gain

The DC-link type quasi-switched boost inverters (DCL-qSBIs) are known as one of impedance-source inverters with low voltage stress on their additional switch, diode, and capacitor. However, the voltage gain of DCL-qSBIs is still low. This paper presents DCL-qSBIs with a modified PWM control strategy. The modified PWM control strategy based DCL-qSBIs improve voltage gain, reduce the conduction loss, and decrease the voltage stress of devices in the switched-boost network. The operating principle of DCL-qSBIs is presented. The 1 kVA-prototype three-phase DCL-qSBI and quasi-Z-source inverter (qZSI) is built to evaluate the performance of the inverters at output voltage ratio of 1.05 to 1.6. The measured efficiency of DCL-qSBI is 0.5 to 1% higher than that of the qZSI at all powers and boost levels tested.
High Efficiency Isolated Resonant PFC Converter for Two-stage AC-DC Converter with Enhanced Performance

This paper presents a high efficiency isolated resonant power factor correction (PFC) converter for two-stage ac-dc converter with enhanced output regulation performance. Unlike conventional boost PFC converters, the proposed PFC converter provides the soft-switching technique to all components operating at the high frequency. As a result, it improves problems by the switching losses and EMI noise in which the conventional PFC converters. In addition, it offers galvanic isolation provided in a dc-dc stage of conventional two-stage ac-dc converters. Thus, due to the high efficiency characteristic and galvanic isolation of the proposed PFC converter, it is possible for designing of a dc-dc stage that focuses on enhancing the output regulation performance. Finally, for the proposed PFC converter, a 1kW prototype is implemented, and its performance and validity are evaluated based on experimental result.
Classifying Human Behaviors: Improving Training of Conventional Algorithms

Research and development of human driving behavior prediction is a typical topic for human behavior recognition in cognitive systems, which plays an important role in the development of Advanced Driver Assistance Systems (ADAS). Routines used are based on machine learning algorithms, which have to be trained first. In this contribution, a strategy for improved training is developed to increase the reliability of the training process with respect to the relevant detection and fault alarm parameters. Four algorithms like Support Vector Machines (SVM), Hidden Markov Models (HMM), Artificial Neural Networks (ANN), and Random Forest (RF) are used as examples. Three lane changing behaviors (left/right lane change and lane keeping) are modeled as classifications, and simulated on a highway scene using driving simulator.To improve the prediction performance of the related models, design parameters, which are unknown and need to be set manually before training, are modified. Using the proposed training procedure the most suitable design parameters can be determined automatically to optimize the performance of the algorithms. Based on data achieved from 7 different drivers four improved models are validated. The finally obtained results show that the prediction performance of the four algorithms can be improved using the proposed optimized training procedure.
A Zero-Shot Intrusion Detection Method Based on Regression Model

Intrusion detection has always been a hot and difficult topic in the field of computer security. It is difficult to use traditional intrusion detection methods to effectively detect unknown intrusion types. To solve this difficulty, in this paper, a zero-shot intrusion detection method based on regression model is proposed to identify unknown intrusion types in order to provide guarantee for computer security. The method includes firstly taking the data in the normal state and the known intrusion type state as the training set. If the features are non-numeric, one-hot code is used to convert the non-numeric features into numerical features. In addition, in order to overcome the shortage of small data volume of some intrusion types, A Markov model based on exponential smoothing method is proposed. According to the numerical value of the features in the training set, the regression equation was fitted for each state category. Using the numerical value of the features in the training set, the threshold value corresponding to each state category is calculated. For a specific state to be tested in the test set, the regression equation of each state category is substituted successively, and the calculated results are judged to meet the threshold requirements, so as to recognize which state it belongs to: normal state, known invasion state or unknown invasion state. Experiments show that the method proposed in this paper is effective to some extent.
Efficient Task Assignment for Crowd-Powered Rebalancing in Bike Sharing Systems

Bike Sharing Systems (BSSs), serving the cycling trips, have become an important alternative for addressing the last mile problem in city Intelligent Transportation Systems (ITSs). However, it is difficult to achieve a desired quality of service due to the unbalanced distribution of station inventory, which is caused by dynamic usage patterns. Different from conventional redistribution strategies performed by trucks or trailers, we propose a Prediction-Based Task Assignment (PBTA) scheme to outsource the rebalancing tasks to crowds so as to decrease the maintenance and labor cost. The Long Short-Term Memory (LSTM) is first utilized to predict the future fill-levels of the bike stations. Then, the rebalancing problem is reformulated as a maximum weighted bipartite matching problem, which is solved by the Hungarian method in polynomial time. Extensive simulations with real-world bike-sharing datasets are conducted to show the effectiveness of the proposed scheme.
Glass Solutions for Wafer Level Packaging

Glass substrates with fine-pitch through-glass via (TGV) technology give an attractive approach to wafer level integration. Glass can be made in very thin sheets (<100 um thick) which aids in integration and eliminates the need for back-grinding operations. Electrical and physical properties of glass have many attractive attributes such as the ability to provide low loss performance, adjust thermal expansion properties and low roughness with excellent flatness to achieve fine L/S. Furthermore, glass can be fabricated in panel format to reduce manufacturing costs. The biggest challenge to adopting glass as a packaging substrate has been the existence of gaps in the supply chain, caused primarily by the difficulty in handling large, thin glass substrates using standard automation and processing equipment. This paper presents a temporary bonding technology that allows the thin glass substrates to be processed in a semiconductor fab environment without the need to modify existing equipment. The approach utilizes a thin inorganic adhesion layer to bond a thin glass wafer to a silicon or glass handle wafer (Si handle wafer is the primary approach). The thin glass substrate, which may contain through-glass vias (TGVs), is then processed through downstream steps such as via fill, CMP, RDL/passive deposition, lithography and bumping. The bond is stable (remains temporary and without outgassing) to over 400°C. Utilizing a Si handle wafer allows the thin glass to be processed leveraging existing processes, with only a mechanical de-bond to yield finished substrates. An attractive benefit to this approach is that it lends itself to creating wafer-level multi-layer stacks. We will provide an overview of the technology and examples of the demonstrated process.
Análise histológica da neoformação óssea com o uso de enxerto bovino liofilizado

Ph.D. Thesis Postgraduate Program in Surgical Sciences Federal University of Rio Grande do Sul HISTOLOGICAL ANALYSIS OF BONE NEOFORMATION WITH THE USE OF LYOPHILIZED BOVINE XENOGRAFT AUTHOR: TIANGO AGUIAR RIBEIRO SUPERVISOR: CARLOS ROBERTO GALIA Porto Alegre, January 13, 2015. Arthroplasty is, ultimately, the final option for the treatment of osteoarthritis. With the increasing number of indications of this procedure, the exchange (total hip arthroplasty revision surgeryTHARS) also became more frequent. The bone defects are problems that can be encountered when conducting a THARS and this defect must be repaired, in other words, the patient's hip needs to be rebuilt before receive a new prosthesis. Most of techniques used to rebuild requires the use of bone graft, and because of this reason, this tissue has become one of the most transplanted tissues today. However the demand for the use of grafts, mostly from bone tissue banks, has increased, but the supply is insufficient. Therefore it is necessary to search for new technologies and alternatives to tissue banks. The bovine lyophilized xenograft (BLX) is one of these options; the production on free demand, its physical and chemical characteristics similar to human bone and its good clinical and radiological outcomes makes it a viable alternative. The aim of this study was to verify and quantify new bone formation by the histological analysis in subjects who received the BLX. This was a case series from July 2000 to April 2013 realized in the Hospital de Clínicas de Porto Alegre. This study included patients who underwent to a THARS where was used the BLX, which later was admitted to a second THARS surgery, not related to the xenograft failure but a mechanical failure of the implant. In this second surgery was performed the bone biopsy. Fourteen subjects were analyzed, 64.3% were female. The average age of patients was 52.36 ± 18.55 years. New bone formation was present in 85.7% of subjects, and constituted 61.79% of the total bone matrix. The diagnosis of BLX absorption was present in 12 subjects. A strong inverse correlation founded by the Pearson's test was observed between the proportion of new bone and the proportion of BLX (p=0.001). No inflammatory response was found. Was concluded that there was suitable bone formation in the vast majority of the cases, as well as the BLX is a good osteoconductive scaffold and can be considered an alternative to other bone graft in the treatment of bone defects.
Mental Workload Assessment During Physical Activity Using Non-linear Movement Artefact Robust Electroencephalography Features

Assessment of mental workload is crucial in safety-critical applications. Often, such applications require the user to be ambulant, such as first responders (e.g., paramedics, firefighters, or police officers). Typically, mental workload models have relied on electroencephalography (EEG) signals. EEGs, however, are known to be highly sensitive to movement artefacts, thus limited applications exist for ambulant users and studies have mostly occurred in controlled laboratory settings. In this paper, we explore the robustness of new non-linear features against movement artefacts and test their effectiveness in monitoring mental workload for ambulant users with the end goal of developing mitigation measures based on mental state of operators. To this end, an EEG experiment was conducted where mental workload and physical activity levels were modulated simultaneously and data was collected from 48 participants. Classical EEG features used for workload assessment, such as spectral power and amplitude/phase coherence, were used as benchmarks and compared against the proposed non-linear multi-scale permutation entropy features. Experimental results show the proposed features consistently outperforming the benchmark ones, thus high-lighting their robustness to movement artefacts.
Comparative Study of Electromagnetic Force Characteristics of Flux Reversal PM Machines with Asymmetrical and Symmetrical Stators

This paper compares the electromagnetic force characteristics of flux reversal permanent-magnet machines (FRPMMs) with the asymmetrical stator (AS) and symmetrical stator (SS) topologies. The electromagnetic forces of the FRPMM are analytically derived by the Maxwell tensor method, and the frequency as well as spatial radial stress harmonic characteristics are obtained. Then, the electromagnetic force characteristics of the two kinds of FRPMMs with different stator-slot/rotor-pole (S/R) combinations are investigated and compared by analytical method and finite-element (FE) analysis. Finally, the electromagnetic forces on the armature teeth and auxiliary teeth of AS- and SS- FRPMMs are evaluated and compared to support the above findings.
Brain-based control of car infotainment

Nowadays, the possibility to run advanced AI on embedded systems allows natural interaction between humans and machines, especially in the automotive field. We present a custom portable EEG-based Brain-Computer Interface (BCI) that exploits Event-Related Potentials (ERPs) induced with an oddball experimental paradigm to control the infotainment menu of a car. A preliminary evaluation of the system was performed on 10 participants in a standard laboratory setting and while driving on a closed private track. The task consisted of repeated presentations of 6 different menu icons in oddball fashion. Subject-specific models were trained with different machine learning approaches on cerebral data from either only laboratory or driving experiments (in-lab and in-car models) or a combination of the two (hybrid model) to classify EEG responses to target and non-target stimuli. All models were tested on the subjects’ last in-car sessions that were not used for the training. Analysis of ERPs amplitude showed statistically significant $(\mathrm{p}\lt 0.05)$ differences between the EEG responses associated with target and non-target icons, both in the laboratory and while driving. Classification Accuracy (CA) was above chance level for all subjects in all training configurations, with a deep CNN trained on the hybrid set achieving the highest scores (mean CA = 53 ± 12 %, with 16 % chance level for the 6-class discrimination). The ranking of the features importance provided by a classical BCI approach suggests an ERP-based discrimination between target and non-target responses. No statistical differences were observed between the CAs for the in-lab and in-car training sets, nor between the EEG responses in these conditions, indicating that the data collected in the standard laboratory setting could be readily used for a real driving application without a noticeable decrease in performance. However, refining the model with real-world data might be beneficial. While there is still room for improvement, the results demonstrate the feasibility of a brain-based control of the car infotainment while driving.
Coarse-to-Fine Luminance Estimation for Low-Light Image Enhancement in Maritime Video Surveillance

Captured images in maritime video surveillance under non-uniform illumination conditions easily suffer from low contrast and details loss. The low-quality images may significantly result in negative effects in practical applications, e.g., target detection, recognition, classification and tracking, etc. Increasing attention has recently been paid to improve the quality of low-light images via computer vision techniques. In this paper, we propose to establish a two-step luminance estimation framework to enhance low-light images. In particular, the coarse luminance is firstly estimated using traditional Max-RGB which extracts the highest pixel values in each color channel. The refined luminance is obtained by introducing a weighted variational model which has the capacities of structure-preserving and texture-smoothing. Based on the estimated well-constructed luminance, the enhanced low-light images are obtained by combining Retinex model with its extended version. The image quality is further improved through a BM3D-based denoising approach. Experimental results on both synthetic and realistic low-light images have demonstrated the satisfactory imaging performance in terms of quantitative and qualitative evaluations.
Online Parameter Identification of Permanent Magnet Synchronous Machines with Nonlinear Magnetics based on the Inverter Induced Current Slopes and the dq-System Equations

This paper shows a fast online parameter identification method for permanent magnet synchronous machines (PSMs), which uses the PWM (pulse width modulation) excitations and no additional test signals. Based on the inverter induced current slopes, the corresponding applied voltages, the rotor angle and a precise machine model, the model's parameters are calculated. Thereby the machine model consists of the dq-system equations, linearized within one PWM period. For stable and precise identification of the PSM machine parameters, the machine model has to be modified with a regularization approach. The identified parameters enable self-commissioning, tuning of the control parameters, condition monitoring or inner fault detection. In this paper the theoretical approach of the suggested method and simulation results of an equivalent test-bench system are presented.
Transient Stability Analysis of Virtual Synchronous Generator Connected to an Infinite Bus

Virtual synchronous generator (VSG) imitates the swing equation of a real synchronous generator (SG) to provide inertia and damping for the utility grid. Therefore, similar to SG, VSG may also lose transient stability under large disturbances. This paper establishes a second-order model of a single-VSG connected to an infinite bus and proposes multiperiod analysis theory (MPAT) of transient stability. Moreover, the influence of system parameters on transient stability is studied. Finally, the effectiveness of theoretical analysis is validated by simulation.
Pancreatite aguda grave em centro de terapia intensiva : análise de 10 anos

OBJETIVO: Avaliar etiologia, complicações, tratamento, tempo de internação – hospitalar e em centro de terapia intensiva – e mortalidade de todos os pacientes internados por pancreatite aguda no centro de tratamento intensivo do Hospital de Clínicas de Porto Alegre, no período de janeiro de 1990 a dezembro de 1999. MATERIAIS E MÉTODOS: Realizamos um estudo de coorte histórico, no qual foram avaliados 57 pacientes, 37% do sexo feminino e 63% do sexo masculino, com média de idade de 48 ± 17 anos. Os pacientes foram divididos em dois grupos – sobreviventes (n=26;45,6%) e não-sobreviventes (n=31;54,4%) –, e foram comparados quanto a tempo de internação, critérios de Ranson e de Glasgow modificados, APACHE II (acute physiology and chronic health evaluation), falências orgânicas, procedimentos cirúrgicos, nutrição parenteral e antibióticos recebidos RESULTADOS: As etiologias mais freqüentes foram alcoólica (37%) e biliar (31%). A mortalidade foi de 54,4%. Os sobreviventes apresentaram maior tempo de internação que os não-sobreviventes (47 ± 36 dias contra 21 ± 20 dias). Os não-sobreviventes apresentaram maiores taxas de falências orgânicas (respiratória, renal e cardiovascular) e maior número de critérios de Ranson e de Glasgow modificados, quando comparados aos sobreviventes. Os parâmetros restantes foram semelhantes entre os dois grupos. CONCLUSÕES: Para melhor avaliar os motivos da alta taxa de mortalidade identificada neste grupo, neste período, seria necessário um trabalho prospectivo com melhor controle dos fatores interferentes e que incluísse ainda a avaliação dos casos de pancreatite aguda com critérios de gravidade que não são admitidos no centro de tratamento intensivo.
CRLB-based Positioning Performance of Indoor Hybrid AoA/RSS/ToF Localization

Fingerprinting indoor localization provides high positioning accuracy with low cost and easy deployment. Considering the unsatisfying precision of received signal strength (RSS)-based fingerprinting, hybrid metrics including angle-of-arrival (AoA) and time-of-flight (ToF), are incorporated to the RSS fingerprinting system. To evaluate the positioning performance of hybrid metrics, the closed-form Cramér-Rao lower bound (CRLB) is derived in this paper. The existence conditions of CRLBs, as well as the relationship of the CRLBs between single and hybrid metrics is revealed. Numerical results based on an office building scenario show that hybrid metrics greatly improve the positioning performance and the robustness to measured standard deviations compared to the single metric’s case. Furthermore, hybrid schemes of the AoA/RSS/ToF metrics are also investigated, and simulations reveal that the scheme of AoA/ToF-supporting access points (AP) enhanced with single RSS-supporting APs achieves the best positioning accuracy among all hybrid schemes.
Design of NAND Logic Gate Using a Single Molecule Transistor

Here we propose to study the I-V characteristics of the three-terminal molecular devices. The 1.4-Benzenedithiol-Molecular Field Effect Transistor (MFET) model is implemented using Matlab simulator. The modeling results showed good performance. In addition, we used our model under VHDL-AMS to create a logic gate. Specifically, we discussed in detail the operation principle of molecular NAND logic gate.