nils-herrmann/clean_scirepeval huggingface dataset:
A Model of MDVRPTW with Fuzzy Travel Time and Time-Dependent and Its Solution

The Multi-Depot Vehicle Routing Problem with time-dependent and fuzzy travel time is very difficult to solve to optimality even for relatively small size instances. So few or no literatures have focused on the problem so far. But it is very close to real world and can make the schedule more availability and more flexible. So this paper focuses on modeling and solution of the problem. A model of MDVRPTW with time-dependent and fuzzy travel time is established. Many factors, which include time-dependent problem, fuzzy travel time problem and FIFO problem, are taken into account. Then a hybrid genetic algorithm, which is seasoned with the model and combined with ant colony algorithm, is presented. The computational results show that the approach has good computation performance and acceptable computational time.
Image Processing Method for Weld Quality Inspection System of Tailored Blanks Laser Welding

the inspection of weld seam is important for high quality of welding. This paper deals with the issue of visual measurement and inspection of weld bead and defect detection in tailored blanks laser welding. A visual inspection system based on laser structured light and gray image has been developed for weld bead profile monitoring, measuring and defect detection. Image processing and extraction algorithms of the laser profiles and feature points are presented. Experiment was conducted with the visual inspection system using structured light and gray image and experimental results validate the effectiveness of the method.
Light Rail Intelligent Dispatching System Based on Reinforcement Learning

The light rail (LR) regarded as the ideal solution for a city that has grown too large for buses, yet cannot be supported by heavy rail with fully dedicated light rails and elevated guide-ways. To improve the efficiency of LR, it is important to develop appropriate dispatching system. The light rail intelligent dispatching system (LRIDS) proposed in this paper is established according to the status of light rail operating including vehicle location and number of passengers, making the best use of reinforcement learning (RL). The algorithm uses a team of RL agents, each of which is responsible for controlling one route. Finally, the developed algorithm is implemented with light rail network of Beijing City. The results demonstrate the power of RL on light rail dispatching problem
A novel biomimetic jellyfish robot based on a soft and smart modular structure (SMS)

This paper introduces the design, fabrication and experiment of a biomimetic jellyfish robot based on a soft and smart modular structure (SMS) to mimic the behavior of the alternating shrinkage and expansion of the real jellyfish. The SMS consisted of two layers including an actuating layer and a recovery layer. And to mimic the actinomorphic structure of the jellyfish, the jellyfish robot had six SMSs or tentacles symmetrically extending from its central disc and a bell covering the SMSs and one central disc. We measured the force acting on the end of the SMS and results showed the force was around 1N and varied with the input current. The force increased as the heating input current increased, and the amplitude of the force gradually reduced and stabilized. Besides, the vertical floating displacements of the jellyfish robot at different frequencies were measured. As results showed that, the floating velocity of the robot was variable. The robot was able to achieve the maximum velocity of 111 mm/s at the frequency of 1.0 Hz, about 1.6 body length per second. Compared with current state of the art of robotic jellyfish actuated by smart actuators, the jellyfish robot achieved the most proficiency at 1.6 s−1. In the case where SMSs were actuated at 1.0 Hz, robot shows better performance in comparison with other frequencies based on the Strouhal Number. Finally, the jellyfish robot was able to accomplish three-dimensional locomotion with the input of sequential pattern.
Design and Implementation of a Maximal Length Nonlinear Pseudorandom Sequence

Pseudorandom Sequences have been widely used in many areas such as telecommunication, cryptology, navigation and radar. m-sequence is one of desirable pseudorandom sequences. By utilizing the feedback functions of m-Sequences, a new method of constructing the feedback function of the nonlinear maximal length shift registers is presented. The feedback function is constructed by using of both the characteristic function and feedback functions from m-Sequences. Through the feedback functions, the nonlinear maximal length pseudorandom sequences are realized on FPGA. Meanwhile, the corresponding analysis demonstrates that this type of sequence has the desirable pseudorandom characteristic. It’s satisfactory and desirable.
On energy efficient design for dynamic CoMP transmission in K-tier heterogeneous cellular networks

Motivated by the objective of pursuing revenue, improvement in coverage and reduction in energy cost for wireless communication networks have been of great significance for mobile operators. Therefore, heterogeneous cellular networks (HCNs) and Coordinated Multipoint (CoMP) transmission are considered as promising solutions to enhance the performances of wireless communication systems. This paper analyzed the K-tier HCNs with a dynamic downlink CoMP scheme, in which the flexible clusters of cooperative stations are determined by a connecting threshold 0. Using stochastic geometry, the coverage probability (CP) and energy efficiency (EE) of a K-tier HCN operating under this scheme are derived, based on which the trade-off between CP and EE is discovered and discussed. Simulation results show the validity of our derivations. The proposed schememay significantly reduce energy consumption sacrificing a small amount of CP, and outperforms the fixed scheme as well. The CP-EE trade-off are also revealed, whichsuggests suitable trade-off points between CP and EE that will deliver the maximum economic profitability. Tendencies discovered in this paper may provide the operators with opportunities for further optimization in pursuit of economic profitability.
Power management for hybrid fuel cell system

This paper proposes a power management control scheme for hybrid fuel cell power system. The hybrid fuel cell system consists of a fuel cell, an isolated unidirectional converter, a bi-directional converter, an inverter, and a battery. The fuel cell powers the steady state energy and the battery compensates the dynamic energy. They need to work together to ensure the system operates with high efficiency and behaviors good dynamic performance. Therefore, the power management should be employed. The goal of the power management control scheme is to control the bi-directional converter operates under buck, boost or shut-down mode according to the condition of fuel cell and battery, so that the battery can compensate the dynamic energy. A 1 kW fuel cell power system was built in the lab. Experimental results are shown to verify the theoretical analysis.
Scattered Points Denoising of TC-Bézier Surface Fitting

Fit TC-Bézier surface with radial base function neural network, and build a radial base function network model which is suitable for surface reconstruction. A method is proposed in this paper, which suggests how to denoise and reconstructing free surface with radial base function neural network. This method simulates the internal relation between the points on surface using the study and training of scattered points made by neural cell. The result of simulation experiment shows that this model has strong capability of fitting surface, keeping the topological characteristic of the original points basically, and it also has function of smoothing noise. Besides, the speed of studying is fast and it can get surface of good fairness.
A Compact Transition Structure Integrated with DC Feed Filter for Submillimeter Wave Application

A compact transition structure integrated with DC feed low pass filter for submillimeter wave application is designed and simulated. In this structure, the probe transition for suspended microstrip is in longitudinal orientation relative to the waveguide, and a sector suspended microstrip low pass filter is integrated for DC feed circuits. Broad bandwidth with low insert loss (<0.1dB) and high return loss (<-20dB) is achieved by using the commercial simulation software HFSS.
A novel DE-PCCM feature for EEG-based emotion recognition

Emotion recognition is a key work of research in Brain Computer Interactions. With the increasing concerns on affective computing, emotion recognition has attracted more and more attention in the past decades. Using electroencephalogra-phy(EEG) is a common way to distinguish emotions although it is also a challenging task. In this paper, we proposed a novel feature called DE-PCCM to improve the accuracy. The basic idea of DE-PCCM is to reveal the relationship between channels after extracting the differential entropy (DE) feature. The DE-PCCM feature can transform the DE features into 2D images so that it could be used as input of Convolutional neural network(CNN). In addition, we constructed a deep learning model for the DE-PCCM feature. Experiments are carried out on the SEED dataset, and our results demonstrate the superiority of the proposed method.
A Novel Semantic-based CAD System for Mammography

A semantic-based CAD system for mammography is proposed. Semantic layer is added to the computer aided diagnosis procedure by comprehend the lesion symptom of mammogram and clinical information. This provides doctor a complete and standardization semantic interpretation of the case. Final result is obtained through the synthetic analysis of the acquired semantic information by a Bayesian semantic network which constructed under direction of the experts. Besides, the result is graded according to the BI-RADS rating system. Preliminary experiments demonstrate that the system is conducive to the interaction with doctor. It's capable to provide assistant diagnosis for a whole mammary disease case other than a single X-ray image.
Video Adaptation based on Affective Content with MPEG-21 DIA Framework

We present a video adaptation system which takes account of users' preference on video affective content (AC) and limited network resource. AC directly causes user's attention, evaluation and memory, which also provides feasible entry for video highlight. According to user's preference, the proposed adaptation insures the video parcels with AC are allocated as much as possible network resource. The system is implemented with MPEG-21 digital item adaptation (DIA) framework which provides a generic video adaptation solution for all video formats and various usage environments by manipulating on XML files. XML file based adaptation avoids complex video computation. 30 students from various departments were invited to test the system and their responses were positive
Design and analysis of a bionic two-DOF joint for dual-arm manipulator

This article presents a new bionic two-DOF robotic joint mechanism that makes it possible to mimic the humanlike motion completely. The proposed joint generates the two-DOF motion in a spherical surface free by the coupled motions of two independent motor-drive pairs. Also, it has the advantage that the payload ability, output power and stiffness of the mechanism can be increased greatly. Mechanism design will be mentioned in detail. Kinematics is studied to analysis the workspace and motion features of the mechanism. Based on these study, two basic motion modes are presented to express the motion of the proposed mechanism. In addition, basic simulations are performed to confirm the effectiveness of the proposed mechanism.
Broadband LTCC transition from coaxial connector to stripline for 60GHz application

A new coaxial-to-stripline transition based on low temperature co-fired ceramic (LTCC) is proposed and experimentally verified. An additional multilayer dielectric cavity is embedded in the LTCC substrate to improve the impedance and field matching and therefore achieve a wide bandwidth. The measurement results for a back-to-back structure show an a return loss better than 10 dB up to 67GHz.
Data Service Portal for application integration in cloud computing

Cloud computing offers both an opportunity and a challenge to the application interoperability. In a cloud computing environment, it may be easier to integrate software applications than in the traditional enterprise computing environment, if they all run on the same cloud platform, especially, on a SaaS (Software-as-a-Service) platform, following a common programming model. However, making multiple applications running on different computing clouds would make it more difficult to deal with multiple cloud platforms and their differences. This paper presents a novel service and data management platform called DSP (Data Service Portal) in the cloud computing environment that facilitates the integration of applications by sharing their information in a loosely coupled manner. DSP enables applications to collectively exchange data through a platform in a fine-grained access control environment. The applications can work cooperatively, through the data sharing mechanism provided by the platform playing the role of a data broker, without being aware of each other's presence. An individual application can maintain its own data and run independently of the other applications in the platform. The data sharing mechanism enables the applications to offer (or subscribe to) a set of data to be consumed by (or produced by) the other applications in the platform. The platform publishes the data model along with APIs and guidelines for applications to adapt to the DSP platform and get to on-board to the platform for the subscriptions.
A digital multimode Boost converter with wide range of input voltage for an energy recycling load system

This paper introduces a digital multi-mode boost converter with wide range of input voltage for an energy recycling load system. The boost converter is controlled digitally with constant off- time/constant frequency hybrid pulse width modulation (PWM) control. Constant off-time control is to avoid the limit cycle oscillation and gain a high gain when the input voltage is far less than the output voltage. Otherwise, constant frequency PWM is adopted. Finally, A 3.6kW DC/DC boost converter with twelve 300W channels in parallel was built for an energy recycling load system and the experimental results testify it to get high gain up to 49 and high efficiency up to about 98%.
High-Temperature Annealing Behaviors of CO$_{2}$ Laser Pulse-Induced Long-Period Fiber Grating in a Photonic Crystal Fiber

High-temperature annealing behaviors of long-period fiber grating (LPFG) induced by high-frequency CO2 laser pulse in an endlessly single-mode photonic crystal fiber (ESM-PCF) are investigated in this paper. We find that the change difference of effective refractive index between the air holes cladding and the solid core of the ESM-PCF both in grating fabricating and in annealing processes with different mechanisms would cause resonant wavelength red shift. Through annealing the gratings under high temperature at 600°C-650°C, its stability, linearity, and repeatability to temperature and tensile strain responses under high temperature condition can be greatly enhanced. Our study shows that the changes in fiber glass structure would have significant influence on the optical properties of ESM-PCF LPFG and thus will help understanding of the forming mechanisms and physical characteristics of ESM-PCF LPFG. In addition, ESM-PCF LPFGs can be used as temperature or strain sensors after annealing at high temperature of 600°C-650°C.
ANN-GA approach of credit scoring for mobile customers

In this paper, an approach to evaluate the credit level of mobile customers has been proposed, in order that this would help solve the fraudulent problems that puzzled most of the mobile corps. The approach is based on the combination of the artificial neural network (ANN) and genetic algorithms (GA), which are two useful methods in the field of artificial intelligence, so that it could overcome the drawbacks of both ANN, such as its deficiency in solving the multi-peak problems, and GA, such as the lack of precision compared with ANN, Besides, in the paper we also use the fuzzy sets theory to depict the real state of the customers. The feasibility of the approach has been demonstrated with the comparison between the state and, the credit score of customers with actual data. The approach could be easily applied in the mobile corp. to solve the contradiction between the satisfying services and the efficient management
Beam scanning range expansion of liquid crystal based leaky wave antennas

In this paper, an effective and general method to expand the electronic beam scanning range of liquid crystal based composite right/left handed leaky wave antennas (LC-CRLH-LWAs) is proposed based on dispersion sensitivity enhancement (DSE). The proposed method does not need to modify the cell structure of the LC-CRLH-LWA, and the balanced condition and impedance matching property of the original LC-CRLH-LWA can be completely kept. For demonstration, a LC-CRLH-LWA is designed. It is shown that, by adding the DSE elements, the electronic beam scanning range of the LC-CRLH-LWA is extended by more than 56% in the frequency region from 12.1 GHz to 12.75 GHz, where the good impedance matching and balanced condition are kept for both extreme tuning states of LC. Finally, prototypes of the designed LC-CRLH-LWA are fabricated. The final simulated results are in perfect agreement with the experimental results.
An effective algorithm for motion estimation of human faces

In this paper, we propose a fast and accurate block-matching algorithm for motion estimation of human faces via Artificial Bee Colony (ABC) optimization. The mean square error (MSE) is often used as the matching metric in block matching, which, however, has the high computational cost in practice. By using ABC optimization, we introduce a novel and effective block-matching metric. We develop a block-matching algorithm based on the proposed matching metric to improve the motion estimation accuracy of human faces with lower computational cost. Experimental results show that our method could achieve significant improvements over state-of-the-art fast block matching methods for motion estimation, in terms of both estimation accuracy and computational complexity.
Analysis of effects on electricity expenses of high-voltage electricity consumer for light load

When the load of high voltage electricity consumer is low, the line will send large of capacitive reactive power to the power grid for the existing of distributed capacitance, causing lower power factor by the side of metering point substation. The electricity expenses of consumer will increase proportionally. With the case of more electricity expenses for the light-load line, this paper has established the π-type line model, calculating the capacitive reactive power of light-load line and comparing with the measured values, and proposed solutions and improvement to reduce effect on expenses of electricity consumer and maintenance the benefits of electricity consumer and power supply company.
WAM—The Weighted Average Method for Predicting the Performance of Systems with Bursts of Customer Sessions

Predictive performance models are important tools that support system sizing, capacity planning, and systems management exercises. We introduce the Weighted Average Method (WAM) to improve the accuracy of analytic predictive performance models for systems with bursts of concurrent customers. WAM considers the customer population distribution at a system to reflect the impact of bursts. The WAM approach is robust with respect to distribution functions, including heavy-tail-like distributions, for workload parameters. We demonstrate the effectiveness of WAM using a case study involving a multitier TPC-W benchmark system. To demonstrate the utility of WAM with multiple performance modeling approaches, we developed both Queuing Network Models and Layered Queuing Models for the system. Results indicate that WAM improves prediction accuracy for bursty workloads for QNMs and LQMs by 10 and 12 percent, respectively, with respect to a Markov Chain approach reported in the literature.
GMM Supervector Based SVM with Spectral Features for Speech Emotion Recognition

Speech emotion recognition is a challenging yet important speech technology. In this paper, the GMM supervector based SVM is applied to this field with spectral features. A GMM is trained for each emotional utterance, and the corresponding GMM supervector is used as the input feature for SVM. Experimental results on an emotional speech database demonstrate that the GMM supervector based SVM outperforms standard GMM on speech emotion recognition.
Automated Sample Data Selecting from DAS Based on Maximum Entropy Theory

How to selecting sample data set from a DAS automatically is a critical problem for machine learning. In this paper, it is illustrated that measurement data included enough information for modeling through comparing the information extropy of a continuous system with its sampling system. Based on maximum entropy principle, an equipartitional method has been discussed which can be used to collect a small data set as a training sample set from a DAS. Then, an application of this method which be used in an ethylene oxide reactor's modeling has been given. The sample set obtained by this way has a uniform distribution as good as distributing of boundary data points. This application illustrates that this way was effectively for selecting a sample set.And combined with a RBF-BP cascaded artificial neural network, it got a satisfactory prediction result.
A 1V low power sigma-delta modulator based on floating gate MOS transistors

This paper presents a novel low voltage and low power continuous time sigma-delta modulator based on the floating gate MOS (FGMOS) transistors. The performance of the two main building blocks in the modulator, the continuous time integrator and the comparator, are achieved by exploiting the benefits of the FGMOS devices. It is proven that using FGMOS devices improves the circuit linearity and hence increases the dynamic range. The modulator, designed in AMS 0.35 mum technology, works at a supply voltage of 1 V, exhibits a dynamic range (DR) of 67 dB, signal-to-noise- ratio (SNR) of 63 dB and signal-to-noise-and-distortion-ratio (SNDR) of 60 dB of 2 kHz Bandwidth (BW), with a power consumption of 5 muW.
An array optimization algorithm for VLSI layout

The authors propose a new array optimization algorithm which is used to minimize the quadratic architectures in VLSI cell generation. Instead of using simulated annealing technology, they use a deterministic strategy to minimize the area usage. The experimental results indicate that their algorithm, like simulated annealing algorithms, has the property of climbing out of local optimums. However, it requires much less computing time. Some published examples were tested. The results are promising.<<ETX>>
Reentry trajectory planning based on genetic optimization of terminal matching

A reentry trajectory planning method based on genetic optimization of terminal matching has been studied for the lifting reentry vehicle with strong terminal constraints. Considering the lifting reentry characters and mission requires, the reentry trajectory was divided into three phases: initial descent phase, quasi-equilibrium glide phase and terminal matching phase. One-parameter iterative method was used for the rapid trajectory planning of initial descent phase and quasi-equilibrium glide phase. The control variable of terminal matching phase was discretized based on global interpolation polynomial, and then be optimized with genetic algorithm. Simulation results show that this reentry trajectory planning method can generate a high precision reentry trajectory with stringent terminal constraints.