nils-herrmann/clean_scirepeval huggingface dataset:
Detection-Free Multiobject Tracking by Reconfigurable Inference With Bundle Representations

This paper presents a conceptually simple but effective approach to track multiobject in videos without requiring elaborate supervision (i.e., training object detectors or templates offline). Our framework performs a bi-layer inference of spatio-temporal grouping to exploit rich appearance and motion information in the observed sequence. First, we generate a robust middle-level video representation based on clustered point tracks, namely video bundles. Each bundle encapsulates a chunk of point tracks satisfying both spatial proximity and temporal coherency. Taking the video bundles as vertices, we build a spatio-temporal graph that incorporates both competitive and compatible relations among vertices. The multiobject tracking can be then phrased as a graph partition problem under the Bayesian framework, and we solve it by developing a reconfigurable belief propagation (BP) algorithm. This algorithm improves the traditional BP method by allowing a converged solution to be reconfigured during optimization, so that the inference can be reactivated once it gets stuck in local minima and thus conduct more reliable results. In the experiments, we demonstrate the superior performances of our approach on the challenging benchmarks compared with other state-of-the-art methods.
Failure Detection and Adaptive Compensation for Fault Tolerable Flight Control Systems

This paper presents an advanced aircraft flight control system which is capable of providing failure detection, identification, and adaptive compensation in the event of the damage of control surfaces. The new approach is based on the detection of immediate changes of correlation between pitching, yawing, and rolling rates. The failure compensation mechanism is realized by an adaptive wavelet neural net-based proportional-integral-derivative (PID) controller. The fault-tolerant mechanism is activated to regain effective control of the damaged aircraft whenever a control surface failure is detected. Design of a real-time executive kernel based on the RT-Linux operating system is experimentally conducted to realize and verify functions of our proposed design.
Rapid Prediction of RRAM RESET-State Disturb by Ramped Voltage Stress

This letter proposes a novel technique for predicting with high confidence the disturbance of the resistive-switching random access memory (RRAM) RESET state based on ramped voltage stress. The technique yields statistical distributions and voltage acceleration parameters equivalent to those of a conventional constant voltage method. Several ramp rates and acceleration models were validated for the accuracy regarding conversion between the two methods. The proposed method not only reduces the time and cost of reliability analysis but also provides a quantitative link between disturbance properties and the widely available RRAM data measured by a linear voltage ramp. Additionally, the non-Poisson area scaling supports the localized filament model.
Learning contour-fragment-based shape model with And-Or tree representation

This paper proposes a simple yet effective method to learn the hierarchical object shape model consisting of local contour fragments, which represents a category of shapes in the form of an And-Or tree. This model extends the traditional hierarchical tree structures by introducing the “switch” variables (i.e. the or-nodes) that explicitly specify production rules to capture shape variations. We thus define the model with three layers: the leaf-nodes for detecting local contour fragments, the or-nodes specifying selection of leaf-nodes, and the root-node encoding the holistic distortion. In the training stage, for optimization of the And-Or tree learning, we extend the concave-convex procedure (CCCP) by embedding the structural clustering during the iterative learning steps. The inference of shape detection is consistent with the model optimization, which integrates the local testings via the leaf-nodes and or-nodes with the global verification via the root-node. The advantages of our approach are validated on the challenging shape databases (i.e., ETHZ and INRIA Horse) and summarized as follows. (1) The proposed method is able to accurately localize shape contours against unreliable edge detection and edge tracing. (2) The And-Or tree model enables us to well capture the intraclass variance.
Recognition of echo signal applying Wigner's distribution

A key and difficult step of echo signal recognition is to extract the effective characteristic of target reflection signal. The research shows that it can limit Wigner's feature extraction for single frequency and single time. For these reasons, we have posed time-frequency analytical method based on Wigner's distribution transformation in the paper. Two kinds of targets True and False are classified by the method. We apply Fuzzy ART network to test the classification ability. When the ratio of signal and reverberation isn't less than 6 dB, the correct recognition ratio can arrive 81% to original echo signal.
Efficient operation mechanism for downlink MUST

Downlink multi-user superposition transmission (MUST), being seen as a non-orthogonal multiple access technique, allows the information of multiple data layers to be superposed and delivered in the same resource. Downlink MUST is expected to improve the system spectral efficiency (SE), and has been considered as an important feature for LTE-Advanced Pro. The work item for downlink MUST was approved in March 2016, and is ongoing in 3GPP. This paper firstly gives an overview of MUST standardization work in 3GPP, and then proposes an efficient MUST operation mechanism by taking into account the practical aspects such as system complexity and receiver implementation. System level simulations validate that, compared with orthogonal multiple access system, MUST with our proposed operation mechanism achieves 11.81% and 14.82% gain in cell average SE and cell edge SE respectively. Moreover, the proposed operation mechanism can keep the scheduling flexibility for MUST UE (user equipment), significantly reduce MUST signaling overhead, and enable LTE legacy UE to be scheduled as MUST-far UE.
A Parameterized Wildcard Method Based on SDN for Server Load Balancing

With the rapid growth of the Internet services, a better technology to support network traffic management is required. Load balancing is a common strategy for this issue. Due to the development of novel network technology - Software Defined Network (SDN), there are more possibilities and opportunities on this issue. The major feature of SDN is the decoupling of the control and data planes to fulfill the logical centralized control in practical and the paradigm of SDN has demonstrated its programmability and high flexibility on network management. So far, many researchers worked on load balancing using OpenFlow networks. One of the improvements is wildcard rule method and we proposed an improved parameterized approach on the host partition process and the proposed method is more efficient when a larger binary tree is given.
Power flow studies in ship medium voltage DC distribution systems for generator-power converter schemes planning

The medium voltage DC (MVDC) distribution system is a new shipboard power distribution technology that has been extensively discussed and studied by many shipbuilding corporations and ship-owners in recent years. As different types of power converters and AC and DC power distributions simultaneously involved in the system, the system performance for different connections of generators to MVDC converters under varying steady-state operating conditions is essential and important in early the design stage of the electrical power distribution system. This type of problem can be analyzed by performing power flow studies. This paper aims presenting a methodology for power flow studies in ship MVDC distribution systems for system planning purpose. A power flow model of the power converters in steady-state is derived to consider medium and low voltage characteristics of electric power system on this type of ship. An AC/DC power flow solution approach then is developed to incorporate AC-system and DC-system models and the AC/DC interface buses in the analysis for determining an appropriate connection of generators and MVDC converters and power converter setting values. Test results of two alternatives for the direct connection of generators to MVDC converters under varying steady-state operating conditions are presented and compared to ensure the performance of the proposed method.
Estimate the measurement uncertainty of broadband antenna (30MHz to 1GHz) calibration system

According to CISPR 16-1 & ANSI C 63.4 standard requirements, to implement EMI radiated emission measurement needs to use accuracy antenna factor (AF). The Standard Site Method(SSM) of antenna calibration is most popular technology in the EMI sector. In Taiwan, the SSM calibration technology is also to set up in BSMI WUDA Antenna Site to calibrate broadband antennas (30MHz to 1GHz). This paper will simply describe the theme of antenna calibration, introduce the structure of this calibration site and describe the measures used to control its calibration uncertainty below 1 dB.
Personalized Web Service Ranking via User Group Combining Association Rule

Web service plays an important role in implementing Service Oriented Architecture (SOA) for achieving dynamic business process. With the increased number of web services advertised in public repository, it is becoming vital to provide an efficient web service discovery and selection mechanism with respect to a user’s requirement. Considerable efforts have been made to solve this problem among which semantic based web service discovery has been attained much importance by researchers in academic and industry community. However, there is a challenge in the semantic based web service discovery process, that is, among the retrieved set of semantically equivalent web service candidates, how to discern which one is the best? In this paper, inspired by collaborative filtering idea, a web service ranking framework is proposed in which a set of users with similar interest will be firstly identified. Afterwards, association rules will be found out by analyzing all web service composition transactions related to that set of users. By combining user group and association rule mined from that group, a personalized web service ranking mechanism is achieved and the experiment shows the promising result.
A neural network for linear matrix inequality problems

Gradient-type Hopfield networks have been widely used in optimization problems solving. This paper presents a novel application by developing a matrix oriented gradient approach to solve a class of linear matrix inequalities (LMIs), which are commonly encountered in the robust control system analysis and design. The solution process is parallel and distributed in neural computation. The proposed networks are proven to be stable in the large. Representative LMIs such as generalized Lyapunov matrix inequalities, simultaneous Lyapunov matrix inequalities, and algebraic Riccati matrix inequalities are considered. Several examples are provided to demonstrate the proposed results. To verify the proposed control scheme in real-time applications, a high-speed digital signal processor is used to emulate the neural-net-based control scheme.
Efficient energy management for electrical scooters

This paper develops an energy recharging controller (ERC) for electrical scooters (ESs) which includes a recharging control unit, a battery recharging control unit, a voltage-boost control unit, a braking control unit, a motor and a battery. There are three recharging modes in the proposed ERC: low-side driving circuit recharging mode, boost recharging mode, and brake assistant mode. With the proposed energy recharging control mechanism, it is shown that mechanical energy can be efficiently converted into electrical energy for energy conservation and the controller would extend the ES's touring range. To ensure safety of the recharging system during battery recharge, a protecting circuit is developed.
Design of synthetic biological logic circuits based on evolutionary algorithm.

The construction of an artificial biological logic circuit using systematic strategy is recognised as one of the most important topics for the development of synthetic biology. In this study, a real-structured genetic algorithm (RSGA), which combines general advantages of the traditional real genetic algorithm with those of the structured genetic algorithm, is proposed to deal with the biological logic circuit design problem. A general model with the cis-regulatory input function and appropriate promoter activity functions is proposed to synthesise a wide variety of fundamental logic gates such as NOT, Buffer, AND, OR, NAND, NOR and XOR. The results obtained can be extended to synthesise advanced combinational and sequential logic circuits by topologically distinct connections. The resulting optimal design of these logic gates and circuits are established via the RSGA. The in silico computer-based modelling technology has been verified showing its great advantages in the purpose.
Thermal management and novel package design of high power light-emitting diodes

Thermal management and package materials in high power light emitting diodes (HPLEDs) are now critical design issues to limit their luminous intensity, reliability, and applications. In this paper electroless and electroplating techniques (EET) were both applied in fabrication of the red, green, and blue HPLEDs (RGB HPLEDs) chips. The HPLEDs with conventional package structures were fixed on the lead frames by adhesives, but the HPLEDs using EET were fixed on copper substrates without any adhesive resin. In our work, the results show that the thermal resistance of HPLED using EET is much less than one of the HPLED using adhesive resin. The maximum luminous intensity of the single chip HPLED using EET is larger than HPLED with resin/MCPCB (metal core printed circuit board) in room temperature.
Improving performance of superconducting fault current limiter with mutual-inductor

The novel method to improve the limiting capability of DC reactor type superconducting fault current limiter (SFCL) by using mutual inductor is presented in this paper. The configurations of the SFCL are described in detail, in which both a superconducting and a mutual inductor play roles of the limiting elements. The working principle of the SFCL is analyzed theoretically and the mathematical model of the SFCL is built. Simulation has been carried out by using EMTDC in a 10 kV/1 kA three-phase power system. The simulation show good results. The performance of the SFCL with mutual inductor is validated, and the feasibility of the SFCL with mutual inductor is also demonstrated
A transformed Lure problem for sliding mode control and chattering reduction

The sliding mode control problem is investigated with the frequency domain approach. The authors show that the sliding mode control can be transformed into a Lure problem. With this formulation, it is shown that the conventional sliding mode control with a nonlinear sign function can only guarantee to make the overall system stable but not asymptotically stable. The authors also consider a practical situation where the sign nonlinearity may contain a hysteresis loop. For this situation, they show that even if the hysteresis loop is very small, there still exists a limit cycle. However, chattering phenomenon caused by the limit cycle behavior can be eliminated by a certain class of nonlinearities.
Minimum Circumscribed Circle and Maximum Inscribed Circle of Roundness Deviation Evaluation With Intersecting Chord Method

Along with the development of precision machining technology, the quality of cylinder surfaces has become the primary factor in deciding whether the quality of parts meets the requirements of high performance or not. In precision machining of cylinders, the measurement and evaluation of roundness deviation is an indispensable component to quantify form tolerance. This paper focuses on the modeling method of roundness deviation. A new modeling method of an evaluation model, i.e., the intersecting chord method, is proposed to address the minimum circumscribed circle (MCCI) and maximum inscribed circle (MICI) in the evaluation of roundness deviation. The method depends on the geometric structure of the intersecting chords, and the chord is used as the main element of modeling rather than as the characteristic point as in earlier methods. Therefore, the virtual center produced by the structure of intersecting chords can accurately locate the endpoint positions of characteristic chords and the center of the reference circle. The modeling process with intersecting chords does not determine the search direction and step size in each iteration, which can reduce the number of iterations and mitigate the difficulty in modeling. Finally, these theoretical studies are verified through comparative verifications and experiments, and the results show that the intersecting chord method can not only quickly construct the MCCI and MICI models but also accurately quantify roundness deviation.
Regenerative braking-driving control system

Currently, electric vehicle technology is becoming more and more mature. Although the anti-lock braking system (ABS) has been commonly applied, most electric vehicles (EVs) still use traditional hydraulic-based disc braking, which has the drawbacks that vehicle wheels are is easy to skid in the rainy day, and easy to be abraded during emergency brake. As a novel method of braking, regenerative braking has the advantages of compact structure, sensitive response, reliability and controllable braking distance. In this research task, a regenerative driving and braking control system for EVs with satisfactory braking performance is proposed. When braking, a motor is converted into a generator-the acquired energy can be used to generate reverse magnetic braking torque with fast response. On this basis, an anti-lock braking controller is realize. A PID controller is also designed to drive the motor and a fuzzy slip ratio controller is designed and used to obtain the optimal slip ratio. Finally, real-world experiments are conducted to verify the proposed method.
A Discriminative Model for Object Representation and Detection via Sparse Features

This paper proposes a discriminative model that represents an object category with a batch of boosted image patches, motivated by detecting and localizing objects with sparse features. Instead of designing features carefully and category-specifically as in previous work, we extract a massive number of local image patches from the positive object instances and quantize them as weak classifiers. Then we extend the Adaboost algorithm for learning the patch-based model integrating object appearance and structure information. With the learned model, a few features are activated to localize instances in the testing images. In the experiments, we apply the proposed method with several public datasets and achieve advancing performance.
Using forecasting technique in quality function deployment to facilitate dynamic customer needs

A proposed method of using forecasting technique in quality function deployment to facilitate dynamic customer needs based on historical, present and predicted data sets is presented. With the use of the proposed method, the importance of each customer needs is monitored and analyzed and the importance of each design requirements is evaluated in order to fulfill dynamic and future customer needs. Doing so would allow companies to plan customer needs in advance, fulfill their needs and, most importantly, significantly improve customer satisfaction and enhance their competitiveness in the marketplace
Look into Person: Joint Body Parsing & Pose Estimation Network and a New Benchmark

Human parsing and pose estimation have recently received considerable interest due to their substantial application potentials. However, the existing datasets have limited numbers of images and annotations and lack a variety of human appearances and coverage of challenging cases in unconstrained environments. In this paper, we introduce a new benchmark named “Look into Person (LIP)” that provides a significant advancement in terms of scalability, diversity, and difficulty, which are crucial for future developments in human-centric analysis. This comprehensive dataset contains over 50,000 elaborately annotated images with 19 semantic part labels and 16 body joints, which are captured from a broad range of viewpoints, occlusions, and background complexities. Using these rich annotations, we perform detailed analyses of the leading human parsing and pose estimation approaches, thereby obtaining insights into the successes and failures of these methods. To further explore and take advantage of the semantic correlation of these two tasks, we propose a novel joint human parsing and pose estimation network to explore efficient context modeling, which can simultaneously predict parsing and pose with extremely high quality. Furthermore, we simplify the network to solve human parsing by exploring a novel self-supervised structure-sensitive learning approach, which imposes human pose structures into the parsing results without resorting to extra supervision. The datasets, code and models are available at http://www.sysu-hcp.net/lip/.
Reversely switched dynistor: From Si to SiC

The characteristics of the pulsed power switch reversely switched dynistor(RSD) based on Si and SiC are analyzed by contrast in the view of the device models, and the advantages of the RSD fabricated by the wide bandgap semiconductor SiC are illustrated in this paper. By establishing the two-dimensional numerical models of the devices, combined with the external circuit models, the blocking characteristics and the turn-on characteristics could be simulated, respectively. The results show that: (1) SiC RSD could get the same blocking voltage as Si RSD with about 1/10 width and 100 times doping concentration of the drift region. (2) It is important for both Si RSD and SiC RSD to accumulate enough precharges in the triggering process, so as to turn on normally and reduce the residual voltage at the front edge of the pulse. (3) For the quasi-static voltage drop in the turn-on process, only when the voltage rate is high enough(∼>6kV) can SiC RSD have advantage over Si RSD. A kind of compact pulse generation circuits based on the PCB level of RSD are also developed in this paper in order to reduce the volume and enhance the output di/dt.
Queue oscillation suppression in large-scale networks by extended network disturbance

It is quite common that the network environment may exhibit time-varying time delays which is the major source of queue oscillation and may destroy stability for a closed-loop connection. This study proposes a transmission congestion control strategy in large-scale wired/wireless network with transmission control protocol under random early detection. The task of time-delay compensation is accomplished here by introducing a pseudo disturbance, named extended network disturbance (END), which characterises the time-delay effect on the system behaviour, and extending to END-based control design. A feedback controller for the purpose is proposed to stabilise the large-scale system. The developed method has been verified using the network simulator.