Miracle-dz/UniFAR-FTUs huggingface dataset:
Graph-based pattern discovery in protein structures

The rapidly growing body of 3D protein structure data provides new opportunities to study the relation between protein structure and protein function. Local structure pattern of proteins has been the focus of recent efforts to link structural features found in proteins to protein function. In addition, structure patterns have demonstrated values in applications such as predicting protein-protein interaction, engineering proteins, and designing novel medicines. My thesis introduces graph-based representations of protein structure and new subgraph mining algorithms to identify recurring structure patterns common to a set of proteins. These techniques enable families of proteins exhibiting similar function to be analyzed for structural similarity. Previous approaches to protein local structure pattern discovery operate in a pairwise fashion and have prohibitive computational cost when scaled to families of proteins. The graph mining strategy is robust in the face of errors in the structure, and errors in the set of proteins thought to share a function. Two collaborations with domain experts at the UNC School of Pharmacy and the UNC Medical School demonstrate the utility of these techniques. The first is to predict the function of several newly characterized protein structures. The second is to identify conserved structural features in evolutionarily related proteins.
An Adaptive Bilevel Programming Model for Nonparametric Prediction Intervals of Wind Power Generation

It is hard to obtain precise wind power forecasting due to the chaotic nature of weather systems. Prediction intervals become an efficient tool to quantify the uncertainty involved in wind power forecasting. Traditional central prediction intervals are widely produced by forecasters, however, which might be conservative with respect to interval width and not well fit the practical conditions. This paper develops a novel adaptive bilevel programming (ABP) model, with extreme learning machine based quantile regression as the follower's problem and tuning hyperparameters of quantile proportions as the leader's problem. The proposed ABP model aims at minimizing the average interval width subject to well calibration. In order to overcome the difficulties in disposing of the intractable nested structure of bilevel programming, the ABP model is equivalently transformed into a single-level nonlinear programming problem with bilinear terms. An improved spatial branch-and-bound (ISBB) algorithm is proposed to efficiently solve the reformulated bilinear programming problem. In the ISBB algorithm, an innovative bounds tightening method is developed to tighten the convex relaxation of the bilinear constraint and enhance the convergence. Experimental studies based on actual wind farm of USA under four seasons show the significant effectiveness and high robustness of the developed ABP model, as well as the excellent global optimum attainment and convergence performance of the proposed ISBB algorithm. Moreover, the widely used traditional interval scores are first verified to be prejudiced in probabilistic wind power forecasting evaluation.
Face sketch-photo synthesis based on support vector regression

the existing face sketch-photo synthesis methods trend to lose some vital details more or less. in this paper, we propose a novel sketch-photo synthesis approach based on support vector regression (svr) to handle this difficulty. first, we utilize an existing method to acquire the initial estimate of the synthesized image. then, the final synthesized image is obtained by combining the initial estimate and the svr based high frequency information together to further enhance the quality of synthesized image. experimental results on the benchmark database and our new constructed database demonstrate that the proposed method can achieve significant improvement on perceptual quality. moreover, the synthesized face images can obtain higher recognition rate when used in retrieval system.
Power Factor Analysis and Maximum Power Factor Control Strategy for Six-Phase DC-Biased Vernier Reluctance Machines

Different from the regular ac machines, the armature winding current waveforms of dc-biased vernier reluctance machines (DC-biased-VRMs) are sinusoidal with dc bias. The power factor (PF) calculation process also shows great difference. This paper intends to analyze the PF theoretically based on the mathematical model. Then, investigate the approach to optimize the PF and the price to pay if measures are taken to further improve the PF. The PF of the DC-biased-VRM is expressed under synchronous rotating frame. A maximum PF control strategy is proposed by adjusting the distribution of three-dimensional dq0-axis currents. The scale of active power to reactive power is changed, and the PF can be regulated. Experimental results are provided to verify the validity of the theoretical analysis.
Analysis on New Read Disturbance Induced by Hot Carrier Injections in 3-D Channel-Stacked NAND Flash Memory

Read disturbance is analyzed in vertically channel-stacked NAND flash memory, which has string select transistors (SSTs) to access each channel layer independently. Additional read disturbance is observed at the cells adjacent to the selected cell in the unselected channel layers as well as typical read disturbance caused by repetitive Fowler–Nordheim tunneling (F-N) stress. Technology computer-aided design simulations and measurements are performed to investigate the disturbance mechanisms. It is found that bitline-side [common-source line-side] adjacent cell is disturbed by the hot carrier injection (HCI), which results from positively [negatively] boosted channel potential by the rising [falling] of the voltage (<inline-formula> <tex-math notation="LaTeX">${V}_{\text {unsel}}$ </tex-math></inline-formula>) applied to unselected cells, respectively. A novel read operation, in which the selected cells are maintained in turned on state during the <inline-formula> <tex-math notation="LaTeX">${V}_{\text {unsel}}$ </tex-math></inline-formula> rising/falling and then are restored to their own states, is proposed to suppress the occurrence of the HCIs. As a result, it is revealed that the HCI-induced read disturbances are successfully suppressed by the proposed method.
Characterization of SiO2/4H-SiC Interfaces in 4H-SiC MOSFETs: A Review

This paper gives an overview on some state-of-the-art characterization methods of SiO2/4H-SiC interfaces in metal oxide semiconductor field effect transistors (MOSFETs). In particular, the work compares the benefits and drawbacks of different techniques to assess the physical parameters describing the electronic properties and the current transport at the SiO2/SiC interfaces (interface states, channel mobility, trapping phenomena, etc.). First, the most common electrical characterization techniques of SiO2/SiC interfaces are presented (e.g., capacitance- and current-voltage techniques, transient capacitance, and current measurements). Then, examples of electrical characterizations at the nanoscale (by scanning probe microscopy techniques) are given, to get insights on the homogeneity of the SiO2/SiC interface and the local interfacial doping effects occurring upon annealing. The trapping effects occurring in SiO2/4H-SiC MOS systems are elucidated using advanced capacitance and current measurements as a function of time. In particular, these measurements give information on the density (~1011 cm−2) of near interface oxide traps (NIOTs) present inside the SiO2 layer and their position with respect to the interface with SiC (at about 1–2 nm). Finally, it will be shown that a comparison of the electrical data with advanced structural and chemical characterization methods makes it possible to ascribe the NIOTs to the presence of a sub-stoichiometric SiOx layer at the interface.
Methodological research for dynamic integration of modular neural network's sub-networks

A generalized definition of modular neural networks(MNN) that almost include all multi-neural networks(systems) is presented.Some relative concepts and problems of MNN' dynamic integration are briefly analyzed.Five new dynamic integration algorithms are presented for a kind of modular neural networks that are based on the principle of "divide and conguer" and adaptive combination.The main differences among each sub-method are: distance measure(absolute distance measure and relative distance measure) and computing method;selected and integrated number of the candidates(some integrate all networks and the others select only in part);integrated strategy and rule(some are based on data driven or knowledge driven,the others are based on data and knowledge driven). Empirical studies show these algorithms' effectiveness and potential.Meanwhile,a technical approach based on the thought of "experienced-in-one-aspect-and-feasible-in-others" is presented for MNN sub-networks' training.
An Automatic Mixing System for Multitrack Spatialization for Stereo Based on Unmasking and Best Panning Practices

One of the most important tasks in audio production is to place sound sources across the stereo ﬁeld so as to reduce masking and immerse the listener within the space. This process of panning sources of a multitrack recording to achieve spatialization and masking minimization is a challenging optimization problem, mainly because of the complexity of auditory perception. We propose a novel panning system that makes use of a common framework for spectral decomposition, masking detection, multitrack sub-grouping and frequency-based spreading. It creates a well spatialized mix with increased clarity while complying to the best panning practices. Both real-time and ofﬂine optimization-based approaches are designed and implemented. We investigate the reduction of inter-track auditory masking using the MPEG psychoacoustic model along with various other masking and spatialization metrics, extended for multitrack content. Subjective and objective tests compare the proposed work against mixes by professional sound engineers and existing auto-mix systems.
Low-profile WLAN MIMO PIFA Antenna System for Laptops with Large Screen

This paper proposes a low-profile two-element wireless local-area network (WLAN) multi-input multi-output (MIMO) antenna system designed for laptops. The primary and secondary elements are mirror-imaged 3-D metal-plate-loaded planar inverted-F antennas (PIFAs), each measuring only 31 mm × 2 mm × 2 mm. Although structurally simple, each PIFA can generate low- and high-frequency resonant modes through its long- and short-branches, respectively. In addition, the meander shorting metal section can easily be adjusted to shift the resonant frequency and to improve the impedance matching. Without any additional matching circuit or chip components, the antenna can cover the 2.4/5.2/5.8 GHz WLAN bands with a profile height of only 2 mm, which is smaller than the record low in the literature. Moreover, with the two PIFA elements deployed on the left and right sides of the top edge of the display ground plate, the measured isolation is larger than 23 dB in the low-frequency (high-frequency) band, and the measured realized radiation efficiency is at least 52% within all operating bands.
Experimental Demonstration of the Tradeoff Between Chromatic Dispersion and Phase Noise Compensation in Optical FBMC/OQAM Communication Systems

Filterbank multicarrier based on offset quadrature amplitude modulation (FBMC/OQAM) is a promising candidate for optical fiber communication systems, thanks to its better time/frequency confinement compared to other multicarrier modulations. Among linear impairments, the compensation of fiber-induced chromatic dispersion (CD) and laser-induced phase noise (PN) in FBMC/OQAM systems induces a compromise in terms of the number of used subcarriers. Considering a fixed dedicated signal bandwidth, the number of subcarriers is expected to be large enough to allow a better frequency-domain CD compensation. On the other hand, it should be sufficiently small in order for the compensation of common phase error inherent to the PN to work properly. In this paper, we experimentally demonstrate the tradeoff between CD and PN compensation that we previously highlighted by simulations. More specifically, considering a 20-GHz FBMC/4-OQAM modulation and an aggregated laser linewidth of about 200 kHz, we show that the maximum transmission distance reaches 3100 km with nearly 512 active subcarriers and three-tap equalizers, when a maximum 1-dB optical signal-to-noise ratio penalty at a bit error rate of 3.8 × 10–3 can be tolerated. Moreover, we propose and validate a frame structure and an integrated synchronization/channel equalization architecture to implement a standalone demodulation system.
OS‐ELM‐based visual and inertial indoor localisation method with adaptive matching step

Indoor localisation has been a hot research topic because of its importance for location-based services. The proposed method takes affine transformation matrices and inertial information as input vectors to online sequential extreme learning machine (OS-ELM) and outputs relative translation between the two matching frames. The universal approximation capability and extreme learning speed of OS-ELM enable the proposed method to adapt to several matching steps and reduce the localisation error to a low scale. Fuzzy multi-objective decision making and corner detection are also used to improve the final result. Experimental results validate that the proposed method can provide a good localisation accuracy and improve the efficiency of localisation.
A Novel 2-D $3\times3$ Nolen Matrix for 2-D Beamforming Applications

In this paper, a 2-D beamforming phased array using a novel 2-D Nolen matrix network is presented. The Nolen matrix is a novel antenna feeding network composed of only couplers with dedicated coupling ratios and phase shifters. It does not require crossover and load termination compared to other networks based on Butler and Blass matrix. To be specific, the closed-form equations are derived first for uniplanar single $3 \times 3$ Nolen matrix, which is composed of three couplers and three phase delay lines. Most importantly, it is found that the proposed Nolen matrix can employ couplers with arbitrary phase differences to achieve relatively flexible progressive phase delays across the radiating elements, presenting a high degree of freedom on circuit topology and beamforming performance. Then, a 2-D antenna feeding network is designed by stacking and cascading six $3 \times 3$ Nolen matrices, and a 2-D patch antenna array is integrated with the proposed feeding network to generate nine radiation beams with unique directions on azimuth and elevation planes, realizing the 2-D beamforming function. To verify the proposed design concept, a prototype of 2-D beamforming phased array operating at 5.8 GHz is designed, fabricated, and measured, and the experimental results agree well with simulation and theoretical analysis.
Artificial neural network and cost estimation for Cr(VI) removal using polycationic composite adsorbent

A cationic adsorbent, namely polyamine/folic acid composite (PFC) was used to remove Cr(VI) from aqueous solutions. The optimum pH was 2.0, achieving Cr(VI) removal of 99.4% at an initial concentration 200 mg/L, PFC dosage 0.5 g/L and temperature 298 K within 90 min. Under the acidic condition, electrostatic attractions between anionic species of chromium ( HCrO4- and Cr2O72- ) and cations on PFC (NR4+ and H+) enhanced Cr(VI) uptake. Additional removal mechanisms including anion exchange with Cl− (because of the use of epichlorohydrin for PFC preparation), reduction of Cr(VI) to Cr(III) and physical interaction could be involved in the adsorption process. Artificial neural network containing a first layer with five inputs, a hidden layer with eight neurons and the last layer with one output was able to predict the Cr(VI) removal efficiency (R2 = 0.919). The total cost of scaling up the adsorption system was $3.53 per m3 of wastewater containing 200 mg‐Cr(VI)/L.
An Image-Adaptive Digital Watermarking Algorithm Based on Wavelet Transform

This paper addresses the issue of how to determine the capacity and strength of the embedded watermark according to the image's content. With the approach proposed in this paper , the length of the watermark sequence is determined by the size of significant coefficients, which are selected by exploiting the tree-based spatial-frequency feature of wavelet transform. The watermark bits are inserted in the high activity texture regions with the maximum strength of Just Noticeable Distortion (JND) tolerance of the Human Visual System (HVS). So the length and strength of the embedded watermark are both image-adaptive, which greatly improves the perceptual quality and robustness of watermarked image. Through analyzing the statistical property of the watermarked image, the detection threshold is determined according to the false alarm and missed detection rate, the embedded watermark can be blindly detected by the correlation value between the marked image and reference watermark sequence. Simulation results show that the proposed method can achieve a better compromise between the robustness and transparency.
Manual for RSiena

SIENA (for Simulation Investigation for Empirical Network Analysis) is a computer program that carries out the statistical estimation of models for the evolution of social networks according to the dynamic actor-oriented model of Snijders (2001, 2005), Snijders et al. (2007), and Snijders et al. (2010a). This is the manual for RSiena, a contributed package to the statistical system R. It complements, but does not replace the help pages for the RSiena functions! It also contains contributions written earlier, for the manual for SIENA version 3, by Mark Huisman, Michael Schweinberger, and Christian Steglich. This manual is frequently updated, mostly only in a minor way. This version was renewed for RSiena version 1.1-301.
Design of a post-disaster shelter through soft computing

Temporary shelters become a more critical subject of architectural design as the increasing number of natural disasters taking place each year result in a larger number of people in need of urgent sheltering. Therefore, this project focuses on designing a temporary living space that can respond to the needs of different post-disaster scenarios and form a modular system through differentiation of units. When designing temporary shelters, it is a necessity to deal with the provision of materials, low-cost production and the time limit in the emergency as well as the needs of the users and the experiential quality of the space. Although computational approaches might lead to much more efficient and resilient design solutions, they have been utilized in very few examples. For that reason and due to their suitability to work with architectural design problems, soft computing methods shape the core of the methodology of the study. Initially, a digital model is generated through a set of rules that define a growth algorithm. Then, Multi-Objective Genetic Algorithms alter this growth algorithm while evaluating different configurations through the objective functions constructed within a Fuzzy Neural Tree. The struggle to represent design goals in the form of Fuzzy Neural Tree holds potential for the further use of it for architectural design problems centred on resilience. Resilience in this context is defined as a measure of how agile a design is when dealing with a major sheltering need in a post-disaster environment. Different from the previous studies, this article aims to focus on the design of a temporary shelter that can respond to different user types and disaster scenarios through mass customization, using Fuzzy Neural Tree as a novel approach. While serving as a temporary space, the design outcomes are expected to create a more neighbourhood-like pattern with a stronger sense of community for the users compared to the previous examples.
Challenges of the Optimization of a High-Speed Induction Machine for Naval Applications

In several industrial sectors, induction machines are being replaced with permanent magnet based alternatives, owing to the potential for higher power density and efficiency. However, high-speed applications feature a wide flux-weakening region, where advanced induction machines could bring benefits in terms of system-level optimization. This paper gives an overview the technological challenges for high-speed drives with induction machines, materials, simulations and future challenges for the power electronics in these applications. The target application is a high-speed induction machine for a naval turbocharging system. The comparison with permanent magnet synchronous machines will demonstrate how the extended flux weakening operation effectively allows for a weight reduction of the overall system.
Simplified Resonant Parameter Design of the Asymmetrical CLLC-Type DC Transformer in the Renewable Energy System via Semi-Artificial Intelligent Optimal Scheme

Asymmetrical CLLC-type dc transformers (ACLLC-type DCTs) are becoming more and more popular in the renewable energy system, thanks to the bidirectional power transmission (PT), high power density, and low-cost sensorless open-loop control. Nevertheless, the resonant frequency is not constant due to variations of the operation power and temperature in practice, which may make DCT lose its required voltage conversion gain and deteriorate the PT ability. This poses a challenge on the design of circuit parameters, especially when the open-loop scheme is usually recommended for the ACLLC-type DCT in the renewable energy system. Therefore, a semi-artificial intelligence (semi-AI)-based simplified parameter design approach is put forward in this paper for ACLLC-type DCT. It replaces all unknown parameters with two intermediate parameters through certain manipulations, and then utilizes a very simple computer-assisted procedure to optimally design the parameters of the ACLLC-type DCT. In addition, a detailed design example with a special planar transformer is presented via the aid of ANSYS Maxwell to achieve the desired resonant parameters of the ACLLC-type DCT. Finally, the proposed semi-AI-based method is experimentally demonstrated in a real renewable energy system prototype.
DEVELOPMENT AND FIELD TESTING OF ADVANCED CONTINUUM TRAFFIC FLOW MODELS FOR CONGESTED FREEWAYS

Advanced traffic management, simulation, and control schemes require reasonably accurate descriptions of flow dynamics especially at congested conditions. Two new high‐order continuum traffic flow formulations are developed for treating congested flows and complex geometrics such as entrance and exit ramps. The acceleration and inertia effects of the traffic mass are properly addressed in the new formulations. Traffic friction due to lane changing at freeway/ramp junctions is also treated through the use of a friction term in the first formulation and a viscosity term in the second. The new models do not require the use of an equilibrium speed‐density relationship, and thus they require less calibration effort in field applications. Satisfactory results are obtained when the models are tested in situations involving pipeline freeways, entrance ramps, and exit ramps. Field data from both loop detectors and video detectors are used. These tests suggest that the new models are capable of describing traffic d.