Miracle-dz/UniFAR-FTUs huggingface dataset:
Finite Frequency Robust H∞ Filtering for Uncertain Spatially Interconnected Time-delay Systems With Interconnected Chains

This paper deals with the problem of delay-dependent robust Hж filtering for uncertain spatially interconnected time-delay systems (SITSs) with interconnected chains in finite frequency domains. First, by the delay-dependent finite frequency bounded ream lemma (BRL) for uncertain SITSs with interconnected chains, a finite frequency robust Hж filter design method can be derived by solving a set of linear matrix inequalities (LMIs). Finally, a numerical example is provided to clearly demonstrate the effectiveness of the proposed method.
The IGD+ indicator based evolutionary algorithm for expensive multi-objective optimization problems

Due to the high experimental cost in function evaluation procedure, commonly used evolutionary algorithms are difficult to solve expensive multi-objective optimization problems. In this paper, we propose a new modified inverted generational distance based evolutionary algorithm to address this issue. The proposed method has introduced a Kriging model to replace the real model. Considering the better characteristics of the IGD+ indicator, we select the performance indicator to balance the convergence and diversity of the population. In addition, we have embedded the mean value and mean squre error into the IGD+ indicator. Meanwhile, two archives management strategy is introduced to store the non-dominated individuals and update the surrogate model. The proposed algorithm has been conducted on some benchmark test problems compared with four related algorithms. The experimental results have validated that the proposed algorithm is suitable for solving the expensive multi-objective optimization problems.
Fault Detection Method of Modular Multilevel Converter Based on Current Mean Value and SVM

In this paper, the IGBT open-circuit fault characteristics in sub-module(SM) for Modular multilevel converter(MMC) are studied, and a fault detection method based on mean value of the three-phase ac current to extract fault characteristics and support vector machine(SVM) classification is proposed. When a fault occurs, the mean value of three-phase ac current is no longer zero, which can detect the occurrence of the fault. When the open-circuit fault occurs in the SMs of different bridge arms, the mean value of three-phase ac current produces different dc bias components. According to the change rule of the mean value of three-phase current, the fault characteristics are extracted, and then faults are classified with SVM classifier. Finally, experimental results show that the proposed method can detect the fault quickly and classify the faults accurately.
Prototyping NOMA Constellation Rotation in Wi-Fi

Non-orthogonal multiple access (NOMA) is a promising technique for improving the performance of Wi-Fi networks. With NOMA, a Wi-Fi access point may simultaneously transmit several flows. However, the ability of the NOMA devices to recover their frames depends on how the signals are multiplexed. In this demo, we extend our previously designed NOMA Wi-Fi prototype to enable constellation rotation. This feature can combat the phase noise, a detrimental effect for NOMA signals. We experimentally study constellation rotation and prove that it makes NOMA signal reception more robust compared to traditional NOMA11The research was done at IITP RAS and supported by the Russian Government (Contract No 14.W03.31.0019). Video showcase of the demo is available at https://youtu.be/yFobqI7EiSA.
Estimating Conductivity and Interfacial Polarization Loss Factors of Oil-paper Insulation for Power Transformers

This paper proposes the method for categorizing the estimating conductivity and interfacial polarization loss factors of the oil-paper insulation. The simulated dielectric responses in the time domain of oil-paper insulation with various conditions were studied. Lumped equivalent circuit, so-celled extended Debye model, for modeling the oil-paper insulation obtained from simulated results, were estimated by using ohm’s law for conduction current, and a curve fitting technique with exponential functions applied to the depolarization current idep(t). The complex capacitance, conductivity loss factor, and interfacial polarization loss factor of the insulation system in the frequency domain from 0.01 mHz to 1 Hz can be calculated.
Optimal PSS Design Using Particle Swarm Optimization Under Load Shedding Condition

Power System Stabilizer (PSS) is a supplementary control that provides additional control actions on the excitation side of the generator. In this study a Particle Swarm Optimization (PSO) based tuning method is proposed to optimize the PSS parameters. PSO optimization results obtained optimal tuning results and fast computing processes. With optimal PSS parameters, the optimal PSS performance is obtained. The combination of PSS and excitation is used to reduce the oscillation that occurs in the system. In this research a case study of load addition and load shedding is used. From the simulation results it is found that the performance of the system performance is improved by installing Power System Stabilizer which is optimized with PSO. System performance is shown by the response of the generator speed and rotor angle which results in a small overshoot and a faster settling time when there is an increase in load and also load shedding. Increased system performance is also viewed from the negative system eigenvalue, negative eigenvalue indicates the system is stable.
Sliding Mode Adaptive Robust Control for High-Speed Trains Based on Improved Approach Rate

The stability of a nonlinear system is affected by many factors, so it is impossible to establish an accurate mathematical model for it, including the uncertainty of the parameters and structure in the system, which makes the nonlinear system unable to obtain a good control effect. For high-speed trains, the mathematical model changes constantly with different operating environments, so it is very difficult to get an accurate model of each line. The speed error of the train control is generally required to be controlled within 2.5% of the current speed. If the speed is too fast or too slow, it will not only cause inaccurate parking, but also affect train operation efficiency. Because the train's running process will inevitably be affected by external interference and affect the train's control performance, the design of the control method is very important. In this paper, considering various resistance conditions, a sliding mode adaptive robust control with good control effect is proposed for trains. Compared with traditional PID control and traditional sliding mode control, it proves that the proposed method can effectively suppress external interference and accurately track the target curve.
User Association in Hybrid UAV-cellular Networks for Massive Real-time IoT Applications

5G mobile networks will have to deal simultaneously with three types of traffic: mobile broadband communications, critical communications and Internet of Things (IoT). In this paper, we propose a novel hybrid 5G Fiber-Wireless (FiWi) access architecture equipped with Mobile Edge Computing-enabled (MEC) macro base stations and investigate its performance. Given the proposed architecture, we aim to solve the user association problem under differentiated Quality of Service (QoS). Indeed, we aim at maximizing the number of the users that are served by the network within their latency constraints, having IoT and haptic applications' requirements in mind. Our approach to solve the user association problem is based on Genetic Algorithms (GA).
FleN and FlhF as new regulatory elements in the lifestyle switch in Pseudomonas putida

Motivation: Pseudomonas putida is a soil bacterium that can be found in nature as individual motile cells or as part of sessile communities called biofilms (1). Biofilm formation could be considered an adaptive strategy as it provide higher resistance against adverse agents, antimicrobial treatments, ultraviolet radiation or dehydration, increasing bacteria survival (2). The switch from a planktonic lifestyle to biofilm formation in P.putida is regulated by FleQ and the intracellular levels of c-di-GMP (3). Isolation of insertion mutants in flhF gene, defective in biofilm formation, suggests the involvement of additional elements in the regulation of this process. In this work, we have characterized the transcriptional organization of flhA, flhF, fleN and fliA genes and the role of FlhF and FleN in biofilm development in P.putida . Methods: Planktonic growth and biofilm formation curves, adhesion and swimming -motility assays. Gene expression analysis: β -galactosidase assays and RT-PCR. Electrophoretic mobility shift assays (EMSA). Results and Conclusions: To test the role of FlhF and FleN in biofilm development, we carried out a phenotypic characterization of ΔflhF and ΔfleN mutants. Experiments shown that ΔfleN mutant is not able to form biofilm whereas ΔflhF mutant exhibits a wild-type phenotype. Adhesion assays indicate that ΔfleN mutant has a reduced adhesion, whereas ΔflhF can properly adhere to the surface. On the other hand, both mutant show reduced swimming motility. These resuts suggests that both, FleN and FlhF, are involved in swimming motility, but only FleN is necessary for biofilm formation, probably by altering adhesion capacity of the bacteria. Bioinformatic tools predict that flhF and fleN form an operon with the upstream gene flhA and the downstream gene fliA . In order to corroborate this hypothesis, we have done RT-PCR using RNA from the mutants. Results shown that these genes are structured in a single operon: flhAFfleNfliA . To determine a possible regulatory role of these elements in biofilm formation and motility, β -galactosidase assays were performed to analyse the expression of biofilm and flagellum related promoters. Results shown that FleN downregulates both types of promoters whereas FlhF is only involved in the regulation of one flagellar gene. The regulatory role of FleN has been further studied in vitro by EMSA with the regulator FleQ. Results suggest that FleN is required for FleQ to bind to its target promoters.
Event-Triggering Cooperative Output Regulation of Singular Linear Multi-Agent Systems with Zeno-Free Triggering

In this paper, cooperative output regulation problem of singular heterogeneous linear multi-agent systems with directed communication topology is studied. We design a triggering strategy that combines time-triggering and event-triggering so that Zeno behavior can be avoided completely. Compared with the existing literatures, the main characteristic of this paper is to apply the method of time- and event-triggering to singular multi-agent systems. The results show that the proposed triggering mechanism combined with the proposed distributed control law can solve the problem of cooperative output regulation. Finally, numerical simulation is given to show the effectiveness of the controller and the triggering mechanism.
Influence of Cochlear Implantation on Balance Function in Pediatrics.

BACKGROUND AND OBJECTIVE Patients with profound Sensorineural Hearing Loss (SNHL) are susceptible to vestibular disturbances following Cochlear Implant (CI) surgery. This study aimed to evaluate vestibular dysfunctions following unilateral CI in the congenitally deaf children. METHODS This was a cross-sectional study conducted on 24 children (mean age: 10.56 ± 5.49 years old) who underwent unilateral CI and 24 age-matched controls (mean age: 11.13 ± 6.21 years old). Vestibular functions were assessed by Vestibular Evoked Myogenic Potential (VEMP) and Computerized Dynamic Posturography (CDP). The VEMP test was performed for otolith's function (especially saccule) evaluation. Sensory Organization Test (SOT) protocol of CDP was also utilized to differentiate the role of various sensory systems contributing to postural stability. In addition, total equilibrium score was calculated. The variables were comparatively assessed between the two groups. RESULTS The mean p13-n23 amplitude in the CI users was significantly lower than the controls (p<0.05). However, the two groups showed no significant difference in cVEMP latency values (p>0.05). The SOT analysis revealed that 45.83% (11/24) of the CI subjects had some kind of sensory abnormalities: 7 cases (29.17%) vestibular, 2 cases (8.33%) visual, 2 cases (8.33%) vestibular and somatosensory involvements. Furthermore, total equilibrium score was significantly reduced in implanted group than the controls (p<0.001). At least, 70.59% (12/24) CI patients showed abnormal values in the CDP or cVEMP examinations. CONCLUSION This study shows functional vestibular impairments in children who underwent CI. These patients showed significantly increased postural instability which was more evident in dynamic conditions. These findings provide the basis for better pre-operative counseling and postoperative vestibular rehabilitation to CI recipients.
A Soft Sensor Modeling Method Based on Double-Layer Support Vector Machine

In order to effectively monitor the production status and achieve reliable control in industrial production, the timely detection of key quality parameters is very important. However, due to the complex conditions of the industrial site and limited detection technology, real-time on-line detection of some key quality parameters cannot be achieved in the actual production process. With the extensive application of various information systems represented by distributed control systems in the production process, the process data information has been greatly enriched, making data-driven soft measurement technology an important means for online detection of key variables of industrial processes. A two-layer soft measurement modeling method based on support vector machine (SVM) is proposed. One layer is used to analyze the relationship of industrial data in time series and solve the correlation problem of time series. The other layer is used to model and analyze soft measurement to solve the robustness of nonlinear regression model. The simulation results show that the soft sensor model has good performance.
Outdoor Human Footsteps Event and Environment Joint Recognition

Human footsteps contain rich audio information. The analysis of human footsteps can identify the actor's activity event and activity environment at the same time, so that the two independent tasks of sound event detection (SED) and sound scene classification (ASC) can be identified together. In order to realize this multitask recognition including events and scenes, this paper designs a human running and walking data set in a complex and similar environment. Based on this, a cross double footsteps segmentation scheme is proposed to cross-segment the continuous footsteps sound signals. Features are extracted from the perspective of event and environment, and two fusion features are designed in this paper. Multiple recognition models are used to accurately identify tasks. The cross double footsteps segmentation algorithm and fusion features designed in this paper simplify and balance the tasks, so that the multitask design of outdoor human footsteps event and environment joint recognition can achieve accurate recognition without complex models.
Adaptive & Learning-aware Orchestration of Content Delivery Services

Many media services undergo a varying workload, showing periodic usage patterns or unexpected traffic surges. As cloud and NFV services are increasingly softwarized, they enable a fully dynamic deployment and scaling behaviour. At the same time, there is an increasing need for fast and efficient mechanisms to allocate sufficient resources with the same elasticity, only when they are needed. This requires adequate performance models of the involved services, as well as awareness of those models in the involved orchestration machinery. In this paper we present how a scalable content delivery service can be deployed in a resource- and time-efficient manner, using adaptive machine learning models for performance profiling. We include orchestration mechanisms which are able to act upon the profiled knowledge in a dynamic manner. Using an offline profiled performance model of the service, we are able to optimize the online service orchestration, requiring fewer scaling iterations.
Analytic Hierarchy Process (AHP) in Portfolio Selection Based on Information Granularity

Analytic Hierarchy Process (AHP) is a multi criteria decision-making method, which can describe and transform the qualitative problems quantitatively, and then get the quantitative analysis results in accordance with the causal relationship between decision attribute. In this paper, a granular Analytic Hierarchy Process, which introduces the granularity mechanism, is proposed to solve the portfolio selection problem under the mean-risk framework. In the proposed method, the scale value of scheme layer is no longer limited to nine positive integers from 1 to 9, which gives granularity attributes to the comparison of advantages and disadvantages in a specific criterion layer between different schemes. The proposed method reflects small differences between different alternative schemes through granularity attribute, so it can provide rich decision information for decision makers. Three numeric examples from the real-world financial market (China Shanghai Stock Exchange) are provided to illustrate an essence of the proposed method.
A 10-Bit 100-MS/s SAR ADC with Always-on Reference Ripple Cancellation

This paper presents an always-on reference ripple cancellation technique that actively cancels the reference settling error throughout the entire SAR conversion process, thus significantly relaxing reference settling requirement. Equipped with the proposed technique, a 100-MS/s prototype SAR ADC only requires a 0.5-pF decoupling capacitor and an on-chip low-power reference buffer that consumes 60% less power compared to state-of-the-art designs with similar performance. Fabricated in 40nm CMOS, it achieves an SNDR of 56.3dB at Nyquist rate while consuming 1.4 mW, including the reference buffer.
Differences in intramuscular fatty acid profiles among Bos indicus and crossbred Bos taurus × Bos indicus bulls finished on pasture or with concentrate feed in Brazil

Abstract Bos indicus (n = 67) and crossbred Bos taurus × Bos indicus (n = 67) bulls were finished in extensive or intensive conditions to evaluate the effect of genetic differences and finishing system on the fatty acid (FA) composition of intramuscular fat. Finishing system had a more pronounced effect on FA profiles than the genetic group, but the two factors often interacted for both individual and groups of FA. When compared with animals finished intensively, those finished on pasture produced meat with higher concentration of CLA and polyunsaturated n-3 FA, in particular of 18:3, 20:5 and 22:5. Meat from animals finished intensively had higher amounts of 14:0, 16:0, 18:1 trans-10, 18:1 trans-11, monounsaturated trans FA and 18:2. When the two genetic groups were compared under intensive finishing, B. indicus animals showed lower amounts of 20:4 (synthesised from 18:2) and 20:5 (synthesised from 18:3), suggesting that they may have a lower ability in biochemical pathways involved in the metabolism of n-6 and n-3 long chain fatty acids. Overall, meat from animals finished on pasture had a higher amount of the FA considered desirable for human health.
Age-Energy Region in Wireless Powered Communication Networks

This paper explores the age-energy region in a wireless powered communication network (WPCN), where an Internet of things (IoT) device works in a “harvest-then-transmit” protocol, in which the IoT device first harvests energy from radio-frequency (RF) signals transmitted by a hybrid access point (HAP) via wireless power transfer (WPT), and then transmits real-time update using the harvested energy to the HAP. For such a system, achievable age-energy region and age-energy function are defined as metrics to measure the trade-off between the age of information (AoI) and the storable energy at the IoT device. Moreover, the optimal transmission policy is designed by solving an AoI minimization problem formulated in terms of the storable energy at the IoT device, which also characterizes the boundaries of the system's age-energy region. As the problem is non-convex, it is first transformed into equivalent ones, and then solved with the closed-form solution. Numerical results show that the transmit power at the HAP and the distance between the HAP and the IoT device have greater effects on the storable energy than the system AoI, and the data size of the update packet has a greater effect on the system AoI than the storable energy. The minimal signal-to-noise-ratio (SNR) threshold has a bilateral influence on the age-energy region, while the impact of the maximal achievable transmit power of the IoT device on the age-energy region is not obvious.
PA-GGAN: Session-Based Recommendation with Position-Aware Gated Graph Attention Network

Session-based recommendation aims to predict user behaviors based on anonymous sessions. Recently, session sequences are modeled as graph-structured data. Based on the session graphs, Graph Neural Networks (GNNs) can capture complex transitions of items, compared with previous conventional sequential methods. However, the existing graph-construction approaches have limited power in capturing the position information of items in the session sequences. In addition, GNNs employed in the existing session-based recommendation are not capable to attend over their neighborhoods’ features in feature aggregation phase. In this paper, we propose a Position-Aware Gated Graph Attention Network (PAGGAN). Specifically, a reverse-position mechanism is proposed to assign position embeddings to nodes in the session graphs based on the order of items in each session sequence. And we enhance Gated Graph Neural Network (GGNN) by introducing self-attention mechanism when aggregating features from nodes. Experimental results on two real-world datasets show that the PA-GGAN outperforms state-of-the-art methods.
A 247 and 272 GHz Two-Stage Regenerative Amplifiers in 65 nm CMOS with 18 and 15 dB Gain Based on Double-Gmax Gain Boosting Technique

This work proposes the concept of double-Gmax (Gmax : maximum achievable gain) core based regenerative amplifier which, in principle, breaks the gain barrier of Gmax (the highest gain that can be obtained from a single transistor) at the frequencies below the maximum oscillation frequency of the transistor. Regenerative amplifiers adopting the proposed double-Gmax core are implemented in a 65 nm CMOS technology and measurements show the peak gain of 18 and 15 dB, 9 and 7.5 dB per stage, at 247 and 272 GHz, respectively.
Research on Moving Target Tracking System Based on FPGA

In this paper, a fusion algorithm of background difference and Sobel is proposed. This algorithm can not only accurately detect the moving target, but also well describe the appearance and contour of the target, and also eliminate a lot of irrelevant noise. The detection and tracking system is based on the FPGA development platform of DE2-115 development board. Firstly, video images are collected by TRDB-D5M camera, raw image format is converted into RGB format and stored in SDRAM, and image acquisition is realized. The image is read from SDRAM, and then the median filter is carried out after the gray-scale transformation to realize the image preprocessing. Then we use the fusion algorithm of background difference and Sobel edge detection mentioned above, as well as the centroid tracking algorithm to detect and track the moving target. Finally, we display the real-time image and tracking results through VGA. After many times of debugging, the system can process 640 * 480 @ 60Hz image, realize the real-time tracking of moving target, and the tracking rate can reach higher than 90%. The system realizes the expected function and meets the requirements of real-time, and has a large potential for progress and application prospects.