nils-herrmann/clean_scirepeval huggingface dataset:
On the canonical form of neural dynamics and a dual system model for neural networks

Two sets of variables seem to be essential for well defining the dynamics of a neural network model, i.e., the set of activity variables which defines the configuration of the activities of all neurons in the system, and the set of connection variables which prescribes the interactions among the neurons. It is obvious that these two sets of variables are closely related to each other. In this work we present an investigation to the possible theoretical framework for the unified description for the dynamics in both of the two sets of variables. We choose steady states to carry out this investigation.
An Improved Design of Automatic-Identification-System-Based Man Overboard Device: A Multidisciplinary Product

In this paper, the improved design of a small automatic-identification-system (AIS) based man overboard (MOB) device is described. The device is one of the state-of-the-art maritime vehicular technologies and has been widely used in marine vehicles for search and rescue (SAR) purposes. Similar devices have been available in the market for years; however, the design methodology and hardware circuitries have never been exposed in literature. This paper first reviews the basic principles of Gaussian minimum-shift keying modulation and the direct digital frequency synthesis technique. Second, the design methodology and hardware implementation of subsystems of the device with design objectives in effective SAR are presented. Finally, a prototype of the AIS MOB was fabricated and fully tested. The experimental results demonstrate that the emitted signal spectrum meets the AIS standards. The fabricated prototype has a minimum effective range of five nautical miles with a battery life of 36 h under continuous operation. This AIS MOB device is a multidisciplinary design product. The described work comprises of active antenna design, the microcontroller-based AIS control circuit design, the global positioning system circuit design, and the implementation of Gaussian minimum-shift keying modulation.
Effect of water absorption on the stability of a polymer based Young's interferometric sensor

In this work the refractive index change of a polymer (Ormocer) based waveguide interferometer under the influence of water absorption was analyzed. The water absorption caused refractive index change was up to 10−4. After half an hour contacting with deionized water a pseudo-equilibrium state was reached and the response of the sensor tended to be stabilized. We performed refractive index measurement with glucose water dilutions. The measured refractive index change showed a linear relationship with the glucose concentration.
A Particle Filter Algorithm for Real-Time Multiple Objects Tracking Based on Color Local Entropy

To achieve accurate and real-time visual multi-object tracking, overcome the difficulties brought by the object deformation, occlusion, and illumination variations, a novel particle filter for multi-object tracking algorithm based on color local entropy (CLE) is proposed. This paper starts from improving the description ability of the object feature model. First, the traditional histogram weighted function is optimized. Second, for the shortcoming that the color feature is sensitive to illumination and environmental interference, a new color local entropy object observation model is constructed by mapping the object from color feature space to local entropy space which is stable to deformation and illumination. In addition, in order to make the algorithm better adjust to the object deformation and environmental interference, an adaptive updating strategy of the object model is designed and the number of particle is adjusted dynamically. The experimental results show that the proposed algorithm is effective for the real-time multiple objects tracking and it is applicable to both rigid and non-rigid object.
Fault Detection and Remedy of Multilevel Inverter Based on BP Neural Network

An open fault detection and analysis method for 7-level Cascaded H-Bridge Inverter based on BP Neural Network is proposed in this paper. A reconfiguration method is also discussed. The output voltage is used as a diagnostic signal to detect the fault types and locations. First, the output voltage is transformed by DFT to select the main harmonic information which is then used to train the neural network. After that, the classification task is performed by a BP neural network. If there is a fault, the reconfiguration system would reconstruct the inverter to make it continually work without affecting the inverter performance. The expected and simulation results are in good agreement with each other, which represents the proposed method can perform satisfactorily to detect the fault types and locations as well as conduct reconstruction.
Duty ratio control of a rotary PWM valve with periodic measurement error

The focus of this paper is on estimating system states in the presence of repetitive measurement noise. This estimation technique is applied to a novel self-spinning high speed rotary on/off valve that is being developed for pulse width modulating (PWM) fluid flow. The PWM duty ratio is determined by the valve spool's axial position. Unfortunately, the optical position measurement is corrupted by a repetitive noise, induced by the spool's rotary motion. Two models are developed to represent the periodic noise: one discrete time time invariant model based on internal model principle and a continuous time model that uses a set of periodic basis functions. Kalman filters are designed to estimate the spool position and the periodic noise. This estimates are used with a PI with feed-forward controller for the spool position reference tracking. Simulation and experimental results indicate the usefulness of estimating the periodic noise.
New oscillation emergence in three-group oscillation mode and its control measure research after islanding

Based on an equivalent system model, new oscillation emergence phenomena of three-group oscillation mode are focused after the traditional separation measure in which new oscillation center distribution character and regularity, its impact on separation measure are studied. The simulation results of Northeast Grid, Northwest Grid, East China and North China Grid of China validated the presented new oscillation center distribution character and regularity. The impact of all kinds of three-group oscillation modes on isolation measure and corresponding counter-measure are discussed in which the traditional oscillation mode of three main out-of-step generator groups, the mode of two main out-of-step generator groups with one minor counter-part and the mode of one main out-of-step generator group with two minor counter-parts are included. Simulation result shows that the major out-of-step interface should be the priority of the three-group oscillation mode islanding strategy.
Dynamic Multi-User MIMO scheduling with limited feedback in LTE-Advanced

Multi-User MIMO (MU-MIMO) systems have gained numerous attention from researchers in the past decade due to its substantial gains in the system throughput. Most of the initial research assumes the knowledge of perfect channel state information at the transmitter (CSIT). However, this is considered impractical and research over the past few years has been focused on receivers feeding back limited information to the transmitter. In this work, we consider a transparent MU-MIMO system model with limited feedback. We propose a general framework for dynamic MU-MIMO scheduling with the capability to switch between Single-User MIMO mode and (Multi-Rank) Multi-User MIMO mode without users feeding back additional multi-user information. Specifically, this is done by each user carefully estimating its CQI under the hypothesis of Multi-Rank MultiUser MIMO transmission and taking advantage of the codebook structure. We consider the sum rate of the system assuming each user uses ML and LMMSE receiver and show that our proposed scheduler, with significantly reduced feedback load, outperforms the best-companion user pairing.
Frequency-Fixed Beam-Scanning Leaky-Wave Antenna Using Electronically Controllable Corrugated Microstrip Line

An electronically controllable microstrip leaky-wave antenna (LWA) to steer the radiations at a fixed frequency is presented. The proposed LWA is composed of a corrugated microstrip line loaded by the varactor diodes with triangular-modulated surface impedance. Due to the periodical modulation of the surface impedance, the guided waves can be converted into the leaky-wave radiations efficiently with frequency-scanning property. Furthermore, the surface impedance of the LWA can be reconfigured by changing the capacitance of the varactor diode through dc bias voltage, which will make the radiation beam steer in a large angle range accordingly at a fixed frequency. Both numerical simulations and experimental results show that the radiation beams can be controlled for continuously steering at each frequency from 5.5 to 5.8 GHz by changing the dc bias voltage from 0 to 20 V, in which the scanning angle can reach as high as 45°.
String stability of heterogeneous platoons with non-connected automated vehicles

It is expected that automated vehicles will gradually penetrate on public roads, resulting in mixed traffic in the next decades. This can impact traffic flow operations, especially the roadway capacity and flow stability. It is of paramount importance to understand and predict the implications of automated driving systems on traffic flow at the early design phase to avoid disruptive impacts on traffic. String stability properties of automated vehicle platoons are a fundamental block to understand their traffic flow stability impact. Previous reports on string stability analysis focussed on homogeneous vehicle strings and simplify the time delays in vehicle systems. This work propose an analytical approach to determine string stability conditions for non-connected vehicle platoons with heterogeneous parameters. To this end, a third-order linear vehicle dynamics model is used in the control design and Laplace transform of the spacing and speed error dynamics in time domain to frequency domain enables the determination of sufficient string stability criteria of heterogeneous vehicle strings. The analytical string stability conditions give new insights into the relationship between the string stability properties of vehicle strings in relation to the system properties of time delays and controller design parameters of feedback gains and desired time gap. Analytical results are verified via systematic simulation of both homogeneous and heterogeneous strings. Simulations demonstrate the predictive power of the analytical string stability conditions.
Towards a model and specification for visual programming of massively distributed embedded systems

Massively distributed embedded systems are rapidly emerging as a key concept for many modern applications. However, providing efficient and scalable decision making capabilities to such systems is currently a significant challenge. This paper proposes a model and a specification language to allow automated synthesis of distributed controllers, which implement and interact through formalisms of different semantics. The paper refers to a case study to illustrate the main capabilities of the proposed concept.
Interactive Video Indexing With Statistical Active Learning

Video indexing, also called video concept detection, has attracted increasing attentions from both academia and industry. To reduce human labeling cost, active learning has been introduced to video indexing recently. In this paper, we propose a novel active learning approach based on the optimum experimental design criteria in statistics. Different from existing optimum experimental design, our approach simultaneously exploits sample's local structure, and sample relevance, density, and diversity information, as well as makes use of labeled and unlabeled data. Specifically, we develop a local learning model to exploit the local structure of each sample. Our assumption is that for each sample, its label can be well estimated based on its neighbors. By globally aligning the local models from all the samples, we obtain a local learning regularizer, based on which a local learning regularized least square model is proposed. Finally, a unified sample selection approach is developed for interactive video indexing, which takes into account the sample relevance, density and diversity information, and sample efficacy in minimizing the parameter variance of the proposed local learning regularized least square model. We compare the performance between our approach and the state-of-the-art approaches on the TREC video retrieval evaluation (TRECVID) benchmark. We report superior performance from the proposed approach.
Residual vibration suppression for series elastic actuator based on phase plane analysis and trajectory tracking

This paper proposes a residual vibration suppression method for a series elastic actuator (SEA). The method is made up of two parts: the trajectory planning for motor based on phase plane analysis (PPA) and trajectory tracking with a nonlinear disturbance observer. Though SEA provides some benefits, it suffers from extremely difficult position control issue, especially the residual vibration resulted from the elastic component between the actuator and payload. Therefore, an efficient method for this problem is proposed in this paper. Specifically, the variation law of the SEA in the phase plane is firstly derived via mathematically analysis, then a trapezoidal acceleration trajectory planning method for the motor side of SEA is used based on this analysis. Moreover, a trajectory tracking method with a disturbance observer (NDOB) is proposed. Simulation results illustrate the superior performance of the proposed method for residual vibration suppression.
Palmprint Recognition Based on Complete Direction Representation

Direction information serves as one of the most important features for palmprint recognition. In the past decade, many effective direction representation (DR)-based methods have been proposed and achieved promising recognition performance. However, due to an incomplete understanding for DR, these methods only extract DR in one direction level and one scale. Hence, they did not fully utilize all potentials of DR. In addition, most researchers only focused on the DR extraction in spatial coding domain, and rarely considered the methods in frequency domain. In this paper, we propose a general framework for DR-based method named complete DR (CDR), which reveals DR by a comprehensive and complete way. Different from traditional methods, CDR emphasizes the use of direction information with strategies of multi-scale, multi-direction level, multi-region, as well as feature selection or learning. This way, CDR subsumes previous methods as special cases. Moreover, thanks to its new insight, CDR can guide the design of new DR-based methods toward better performance. Motived this way, we propose a novel palmprint recognition algorithm in frequency domain. First, we extract CDR using multi-scale modified finite radon transformation. Then, an effective correlation filter, namely, band-limited phase-only correlation, is explored for pattern matching. To remove feature redundancy, the sequential forward selection method is used to select a small number of CDR images. Finally, the matching scores obtained from different selected features are integrated using score-level-fusion. Experiments demonstrate that our method can achieve better recognition accuracy than the other state-of-the-art methods. More importantly, it has fast matching speed, making it quite suitable for the large-scale identification applications.
Recognition of Handwritten Characters in Chinese Legal Amounts by Stacked Autoencoders

Handwritten Characters Recognition has long been a tough problem in pattern recognition and machine learning. Some special tasks, such as automatic check preprocessing, require Handwritten Chinese Legal Amounts recognition as a prerequisite. Since we expect to utilize machine instead of human to process bank checks, the recognition rate in such task must reach a relatively high rate. This paper proposes to use deep learning, auto-encoder as an effective approach for obtaining hierarchical representations of Isolated Handwritten Chinese Legal Amounts. Experiments show such representations are highly abstractive and can be used in character recognition. Meanwhile, a novel way by combining multiple Neural Networks in doing the work is proposed which proves to be able to improve the recognition rate significantly. This method reports a 0.64% error rate on a large test set over 10,000 samples and outperforms traditional methods using hand-crafted features and convolutional neural network committees (another deep learning model), narrowing the gap to human performance.
Utilizing Related Samples to Enhance Interactive Concept-Based Video Search

One of the main challenges in interactive concept-based video search is the problem of insufficient relevant samples, especially for queries with complex semantics. In this paper, “related samples” are exploited to enhance interactive video search. The related samples refer to those video segments that are relevant to part of the query rather than the entire query. Compared to the relevant samples which may be rare, the related samples are usually plentiful and easy to find in search results. Generally, the related samples are visually similar and temporally neighboring to the relevant samples. Based on these two characters, we develop a visual ranking model that simultaneously exploits the relevant, related, and irrelevant samples, as well as a temporal ranking model to leverage the temporal relationship between related and relevant samples. An adaptive fusion method is then proposed to optimally explore these two ranking models to generate search results. We conduct extensive experiments on two real-world video datasets: TRECVID 2008 and YouTube datasets. As the experimental results show, our approach achieves at least 96% and 167% performance improvements against the state-of-the-art approaches on the TRECVID 2008 and YouTube datasets, respectively.
Deep Aging Face Verification With Large Gaps

Along with the long-time evolution of popular social networks, e.g. Facebook, social media analysis research inevitably arrived at the era of considering face/user recognition with large age gaps. However, related research with adequate subjects and large age gaps is surprisingly rare. In this work, we first collect a so-called cross-age face (CAFE) dataset, ranging from child, to young, to adult, to old groups. Then, we propose a novel framework, called deep aging face verification (DAFV), for this challenging task. DAFV includes two modules: aging pattern synthesis and aging face verification. The aging pattern synthesis module synthesizes the faces of all age groups for the input face of an arbitrary age, and the core structure is a deep aging-aware denoising auto-encoder ( a2-DAE) with multiple outputs. The aging face verification module then takes the synthesized aging patterns of a face pair as the input, and each pair of synthesized images of the same age group is fed into a parallel CNN; finally, all parallel CNN outputs are fused to provide similar/dissimilar prediction. For DAFV, the training of the aging face verification module easily suffers from the overfitting results from the aging pattern synthesis module, and we propose to use the cross- validation strategy to produce error-aware outputs for the synthesis module. Extensive experiments on the CAFE dataset well demonstrate the superiority of the proposed DAFV framework over other solutions for aging face verification.
A Generalized False Data Injection Attacks Against Power System Nonlinear State Estimator and Countermeasures

This paper develops a generalized framework that allows us to investigate the vulnerability of the power system nonlinear state estimator to false data injection attacks (FDIAs) from the operator's perspective and to initiate some countermeasures. Unlike most existing FDIA methods, which assume a perfect knowledge of the system measurements and topology by a hacker, we derive and analyze the uncertainties for launching successful FDIAs along with their upper bounds. To effectively defend against an FDIA, we propose a robust detector that checks the measurement statistical consistency using a subset of secure PMU measurements. We first show that if these secure PMU measurements are free of bad data while making the system observable, the FDIA is detectable. We then show that detectability is also ensured if these conditions are relaxed while using alternative redundant measurements from short-term nodal synchrophasor predictions together with the robust Huber M-estimator. Numerical simulation results on the IEEE 30-bus and 118-bus systems demonstrate the effectiveness and robustness of the proposed method even the secure measurements contain noise and bad data.
Transmission rank selection for opportunistic beamforming with Quality of Service constraints

In this paper, we consider a multi-cell multi-user MISO broadcast channel. The system operates according to the opportunistic beamforming framework in a multi-cell environment with variable number of transmit beams (may alternatively be referred as the transmission rank) at each base station. The maximum number of co-scheduled users in a cell is equal to its transmission rank, thus increasing it will have the effect of increasing the multiplexing gain. However, this will simultaneously increase the amount of interference in the network, which will decrease the rate of communication. This paper focuses on optimally setting the transmission rank at each base station such that a set of Quality of Service (QoS) constraints, that will ensure a guaranteed minimum rate per beam at each base station, is not violated. Expressions representing the achievable region of transmission ranks are obtained considering different network settings. The achievable transmission rank region consists of all achievable transmission rank tuples that satisfy the QoS constraints. Numerical results are also presented to provide further insights on the feasibility problem.
Tropical Cyclone Forecast using Angle Features and Time Warping

The most popular approach to comparing two given tropical cyclones (TCs) is to measure the distance between various contour points of the TC extracted from a satellite image. However, this measure has a very high computational cost as it involves many point-to-point calculations. Moreover, this measure does not reflect the most distinctive features of a tropical cyclone, their spiral shape. In this paper, we propose the use of angle features and time warping for TC forecast. The gradient vector flow (GVF) snake model is applied to extract the contour points of a dominant tropical cyclone from the satellite image. Dvorak templates are used as references to predict the intensity of the tropical cyclone. Given two sets of contour points, one for each tropical cyclone, we retrieve the similarity of two shapes using angle features found among the successive contour points. We adopt a time warping approach to produce a fast and accurate result. Experimental results have shown that our approach is better than other conventional comparison approaches such as Hausdorff distance measure.