nils-herrmann/clean_scirepeval huggingface dataset:
Current energy management technologies research in China considering EVs integration

Some latest research works on EVs integration in China are presented in this paper. A future energy management framework supporting large-scale EVs fleets is proposed, involving five related topics: 1) modeling of EV swarms charging load based on naturalistic travel patterns; 2) global power flow based assessment and early warning technologies considering real-time traffic information; 3) emergency control by regulating EVs charging infrastructures; 4) optimized coordination with EVs and renewable energies; 5) interaction between power system and transportation system. Finally, some common features of the above researches are surveyed.
Robust H∞ decentralized intelligent control with new learning algorithm for robot manipulators

A novel robust Hinfin decentralized intelligent control (RHDIC) strategy is proposed for the trajectory following problem of robot manipulators. The proposed system is comprised of a computed torque controller and neural robust controller with new learning algorithm. Based on Lyapunov stability theorem, it is shown that the proposed controller can guarantee stability of closed-loop systems and satisfactory tracking performances. The proposed approach indicates that computed torque control method is also valid for controlling uncertain robotic manipulators as long as compensative controller is appropriately designed.
Software-defined QoE measurement architecture

Quality of Experience (QoE) is a measure of a customer's experiences with a service (web browsing, TV broadcast, call to a Call Center). QoE focuses on the entire service experience, and is a more holistic evaluation than the more narrowly focused UX (user experience, focused on a software interface) and CX (customer experience, support focused). Measuring the bottleneck link bandwidth along a path is important for understanding the performance of many services. Existing tools to measure QoE are using distributed network probes to collect network information. It is a very costly monitoring architecture. This paper proposes a cost-effective QoE measurement architecture based on Software Defined Network (SDN) , adopting centralized control mechanism to measure multiple clients simultaneously. Experiments on Wi-Fi environment show great reducing of CAPEX and OPEX.
Adaptive robust control of nonholonomic mobile manipulators with an application to condenser cleaning robotic systems

In this paper, the motion control of nonholonomic mobile manipulators in presence of uncertainties and external disturbances is considered. An adaptive robust controller is proposed to overcome model uncertainties and bounded disturbances. The control design is developed without imposing any restriction on the system dynamics, and ensures the entire state of the system to asymptotically converge to the desired trajectory. The stability of the closed-loop system is provided using Lyapunov stability synthesis. Simulation results on a condenser cleaning robot are presented to show the effectiveness of the proposed scheme.
Design and fabrication of an all-electric tiebarless injection molding machine

The advantages of an all-electric injection molding machine are energy saving, quieter, cleaner and more accurate. In recent years, the development of all-electric injection molding machines progress rapidly. The core technology of the all-electric injection molding machine is using servomotors to drive all the processes such as injection, clamping, plasticating et al.. The injection unit plays a very important role in the injection molding machine. The injection unit controls many parameters such as injection speed, injection pressure, packing pressure et al.. All the parameters will influence the quality of products. The tiebarless structure is used for the clamping unit in our machine. It can provide larger space for large molds in a smaller machine combined with quicker mold changes. Those advantages can save much time. This paper demonstrates our effort in designing and fabricating an all-electric tiebarless injection molding machine, which has six tons of clamping force.
Obstacle detection and recognition in natural terrain for field mobile robot navigation

This paper presents a novel method for real-time obstacle detection and recognition in natural terrain for a field mobile robot using a image information, geometric information and support vector machine(SVM). Firstly, the scene is divided into two distinct regions: interest regions and uninterested regions. Then detected obstacle points are clustered into objects on the basis of their geometric information, i.e., depth and horizontal information, connectivity. The key obstacle characteristics are identified as width, height, their ratio, the ratio of area and depth. In the paper, the SVM method is used to classifying the objects into four classes. In order to determine the slope value, a SVM slope estimation approximation model was also proposed. Experimental results are presented to demonstrate the capability of the proposed approach for recognition of different obstacle in natural terrain.
Reliable and Scalable DHT-Based SIP Server Farm

Session initiation protocol has been widely deployed to enable multimedia communication. To guarantee service reliability and scalability, service providers usually deploy SIP server farms. In this paper, we present a reliable and scalable DHT-based SIP server farm. In the proposed server farm, one-hop DHT is adopted and a new SIP event package called SIEP (server information event package) is designed. In addition, a novel load sharing and failover scheme is designed based on one-hop DHT and SIEP. Compared with multi-hop DHT, one-hop DHT produces less overhead in DHT-based SIP server farm. Using SIEP, DHT routing hop number can be further reduced to zero in registration and intra-domain call setup process. Performance analysis shows the whole message overhead (overhead caused by SIP and DHT messages) can further reduce about 25% by applying SIEP. It's proven that the proposed load sharing and failover scheme is effective, scalable and reliable even in dynamic environments. What's more, it doesn't increase the deployment and maintenance cost.
Modeling of rate and perceptual quality of video and its application to frame rate adaptive rate control

In a prior work, we have developed both rate and perceptual quality models for temporal and amplitude (i.e., SNR) scalable video produced by the H.264/SVC encoder. In this paper, we validate from experimental data that the functional form of the rate model is applicable to H.264/AVC encoded video, which has the same temporal scalability but no SNR scalability, but the model parameter values differ. We further investigate how to predict both rate and quality model parameters using content features computed from the original video. Experimental data show that with proper feature combination, we can estimate the model parameters very accurately, and the estimated bit rate and quality using the predicted model parameters match with the measured bit rate and quality with high Pearson correlation (PC) and small root mean square error (RMSE). We have implemented a simple pre-processor in the H.264/AVC encoder to guide the frame rate adaptive rate control. Results show that our model-based frame rate adaptive rate control outperforms the default rate control algorithm with better quality.
Regional importance analysis under multiple failure modes

For structural system with multiple failure modes extensively present in engineering practice, two regional mode importance measures are proposed on the basis of the existing mode importance measures, i.e., the regional mode importance measure based on failure probability and the regional mode importance measure based on sample mean of output response. Compared with the existing global mode importance measures, the proposed regional importance measures can not only provide the inter-mode importance information, but also reflect the effects of different regions of input variables on the importance of a single mode or a group of modes, thus providing useful information to improve and optimize the reliability model. Numerical and engineering examples are used to demonstrate the feasibility and rationality of the proposed indices.
Subspace learning in the presence of sparse structured outliers and noise

Subspace learning is an important problem, which has many applications in image and video processing. It can be used to find a low-dimensional representation of signals and images. But in many applications, the desired signal is heavily distorted by outliers and noise, which negatively affect the learned subspace. In this work, we present a novel algorithm for learning a subspace for signal representation, in the presence of structured outliers and noise. The proposed algorithm tries to jointly detect the outliers and learn the subspace for images. We present an alternating optimization algorithm for solving this problem, which iterates between learning the subspace and finding the outliers. This algorithm has been trained on a large number of image patches, and the learned subspace is used for image segmentation, and is shown to achieve better segmentation results than prior methods, including least absolute deviation fitting, k-means clustering based segmentation in DjVu, and shape primitive extraction and coding algorithm.
Enhanced parity packet transmission for Video multicast using R-DSTC

In this paper, a cooperative multicast scheme that uses Randomized Distributed Space Time Codes (R-DSTC), along with packet level Forward Error Correction (FEC), is studied. For the source packets, two-hop transmission is considered, where a packet is transmitted first by the access point (AP), and then forwarded using R-DSTC by the nodes that receive the packet. On the other hand, parity packets are generated by the nodes that receive all the source packets correctly and are transmitted using RDSTC. The optimum transmission rates for source and parity packets, as well as the number of parity packets required, are determined such that the video quality at all nodes is maximized. It is shown that this scheme can support a higher video rate than a previously developed R-DSTC based scheme where both source and parity packets go through a two-hop transmission, as well as non-cooperative direct transmission.
Design of the mechanical condition monitoring system for Molded Case Circuit Breakers

In this paper, a mechanical condition monitoring system of Molded Case Circuit Breaker (MCCB) is designed and developed. The operation principle of the monitoring system, the hardware design and the software design are introduced in detail. The three-phase voltage, the voltage between auxiliary normally closed contacts and the motor current are detected during the opening operation, closing operation and reclosing operation of MCCB, wherein these operations are driven by the motor. The mechanical condition characteristic parameters, including closing time, opening time, reclosing time, three phases asynchronous time, closing speed, opening speed, reclosing speed and the force on the handle, can be calculated by analyzing the voltage signals and current signals. The test results show that the system has a good performance. Moreover the characteristic parameters of circuit breaker, obtained in the test, provide test data for the theory research on remaining life prediction of MCCB.
Regularized total least squares reconstruction for optical tomographic imaging using conjugate gradient method

A regularized total least square (RTLS) approach to solve a linear perturbation equation encountered in optical tomography is developed based on the Rayleigh quotient formulation. To compute efficiently the solution, the Rayleigh quotient form of the RTLS filter (RQF-RTLS) is used and a conjugate gradient algorithm is implemented. Simulation results show that the RQF-RTLS method obtains more stable and accurate solutions than the regularized least squares (RLS) approach which does not account for the errors in the operator.
A two-stage video coding framework with both self-adaptive redundant dictionary and adaptively orthonormalized DCT basis

In this work, we propose a two-stage video coding framework, as an extension of our previous one-stage framework in . The two-stage frameworks consists two different dictionaries. Specifically, the first stage directly finds the sparse representation of a block with a self-adaptive dictionary consisting of all possible inter-prediction candidates by solving an L0-norm minimization problem using orthogonal least squares (OLS), and the second stage codes the residual using altered DCT dictionary orthonormalized to the subspace spanned by the first stage atoms. The transition of the first stage and the second stage is adaptively determined based on the estimated residual reduction per bit. We further propose a complete context adaptive entropy coder to efficiently code the locations and the coefficients of chosen first stage atoms. Simulation results show that the proposed coder significantly improves the RD performance over our previous one-stage coder. More importantly, the two-stage coder, using a fixed block size and inter-prediction only, outperforms the H.264 coder (x264) and is competitive with the HEVC reference coder (HM) over a large rate range.
Error-resilient video coding using multiple description motion compensation

A new approach for multiple description video coding is proposed. It employs a second-order predictor for motion-compensation, which predicts a current frame from two previously coded frames. The coder generates two descriptions, containing the coded even and odd frames, respectively. When only a single description (say, that containing even frames) is received, the decoder can only use previous even frames for prediction. The mismatch between the predicted frames at the encoder and decoder is explicitly coded to avoid error propagation in the ideal MD channels, where a description is either received intact or lost completely. By using the second-order predictor and coding the mismatch signal, one can also suppress error propagation in packet lossy networks where packets in either description can be lost. The predictor and the mismatch signal quantizer can be varied to achieve a wide range of tradeoffs between coding efficiency and error resilience.
Remote sensing images classification using fuzzy B-spline function neural network

We propose a remote sensing image classification algorithm using the membership function adjustable fuzzy B-spline function neural network. In the proposed classification algorithm, the fuzzy technique and neural network technique are combined, and fuzzy inference is realized by a neural network. The B-spline function is used as the membership function of the fuzzy neural network and its shape can be adjusted in real time, which endows the classifier with better capability of learning and self-adaptiveness. Experimental results show that the proposed classification algorithm can be used in remote sensing image classification, and its classification precision is superior to that of the conventional maximum likelihood algorithm.
Ecological rehabilitation design of artificial river and seasonal variation of water quality In Tianjin airport economic area

The artificial river in Tianjin airport economic area takes reclaimed water, rain floods and other unconventional sources as landscape water, six years' good operation indicates that the implementation of ecological restoration project can successfully prevent the eutrophication in reclaimed water river, overcome the difficult problem of maintaining water quality. Through site study, we sum up the main ecological restoration technology. There are reasonable layout, ecological riverbank, aquaticanimals and plants, supplemented by artificial waterweeds and aeration techniques in serious part. We also obtain the main engineering design parameters and seasonal changes of river ecology and water quality. This is expected to provide a reference for ecological rehabilitation of similar rivers.
Equipment Productivity Enhancement by SMC (Statistical Machine Control) Application on Furnace Area

In this work, we clearly address the strategies of monitor and via statistic machine control (SMC) on furnace process at ProMOS technology, DRAM manufactory, including low pressure and atmosphere pressure process. Especially for LPCVD nitride deposition process, particle and condense in the piping which was formed by by-product, NH4Cl, is an undesired factor to affect process stability. This work have demonstrated four strategies of statistic machine control (SMC) models, (A)VG2 clog (B) SV clog (C) VV1 clog (D) FCV angle control, to predict the timing and location of by-product clog at LPCVD furnace nitride processes and warm us when the by-produce condense had generated and the clog was accrued. Further more, to predict the schedule to clean the powder by short corrective action. It was found that the counts of leak check fail were obviously getting improvement for different nitride process equipments, the overall improvement of leak check fail average count is 15.8 time/month before May to below 4 time/month. The impacted lots also were reduced 79.3% which equals to 2.6 K wafers move gaining per month. With the reduction of tool down time and maintenance, the productivity can be increased and manpower and cost was saved
Gait and Balance Analysis for Patients With Alzheimer's Disease Using an Inertial-Sensor-Based Wearable Instrument

Despite patients with Alzheimer's disease (AD) were reported of revealing gait disorders and balance problems, there is still lack of objective quantitative measurement of gait patterns and balance capability of AD patients. Based on an inertial-sensor-based wearable device, this paper develops gait and balance analyzing algorithms to obtain quantitative measurements and explores the essential indicators from the measurements for AD diagnosis. The gait analyzing algorithm is composed of stride detection followed by gait cycle decomposition so that gait parameters are developed from the decomposed gait details. On the other hand, the balance is measured by the sway speed in anterior-posterior (AP) and medial-lateral (ML) directions of the projection path of body's center of mass (COM). These devised gait and balance parameters were explored on twenty-one AD patients and fifty healthy controls (HCs). Special evaluation procedure including single-task and dual-task walking experiments for observing the cognitive function and attention is also devised for the comparison of AD and HC groups. Experimental results show that the wearable instrument with the designed gait and balance analyzing system is a promising tool for automatically analyzing gait information and balance ability, serving as assistant indicators for early diagnosis of AD.
Personal assistant robot

This paper presents a design and implementation of a prototype personal assistant robot (PAR). The goal for the development of PAR is to investigate how an intelligent machine can assist and interact with a person in a smart manner. The PAR prototype consists of a mobile platform and a three degree-of-freedom manipulator. Currently, implemented functions include PDA remote control, human face recognition and tracking, and fuzzy behavior-based navigation. Experimental studies of these functions show that the robot demonstrates autonomous interactions as well as assistant functions in a natural indoor environment.
Optimization of spatial, temporal and amplitude resolution for rate-constrained video coding and scalable video adaptation

This paper considers how to choose the frame size, frame rate, and quantization stepsize to optimize the perceptual quality for a given rate constraint. The proposed solution leverages previously developed quality and rate models that explicitly consider the impact of spatial, temporal, and amplitude resolution (STAR) on the quality and rate. Using these models we further propose algorithms for ordering the STAR layers to form a rate-quality optimized stream, which can greatly facilitate scalable video adaptation.
A Neural Network Sliding Mode Controller with Application to Robotic Manipulator

A sliding mode control strategy compensated by neural network is proposed, and that is applied to robotic trajectory control. First, a three-layer neural network is used to compensate the uncertainties in the robotic system. Then the structure of sliding mode controller with neural network compensation and the learning algorithm of the neural network are designed based on Lyapunov theorem to guarantee the stability of the system and improve the dynamic performance of the system. The simulation results show that the proposed control strategy can not only reduce the phenomenon of chattering in effect, but also has good robustness and dynamic performance
Complexity-bounded power control in video transmission over a CDMA wireless network

In this work, we consider a CDMA cell with multiple terminals transmitting video signals. The concept of a utility function is used to maximize the number of received picture frames with adequate quality per Joule of energy. For a reconstructed signal at the video decoder, the quality is controlled by the encoded bit rate, compression complexity as well as received signal-to-interference-noise ratio (SINR). In this work, video quality is measured in peak signal-to-noise ratio (PSNR) rather than SINR. We find that for a given compression complexity, maximum utility is achieved when the product of bit rate and required SINR is minimized. This maximum usually occurs at maximum video coding complexity. We also investigate the capacity in terms of the number of users that can be supported simultaneously for this system, and how the total utility varies with the number of users in this system. This can be used as an admission policy by the central station.
The Key Technology of the New Generation Business System Used in the Domestic Commercial Bank

The process restructuring of commercial banks is a significant transform for China's financial enterprises operating mode, and the IT platform construction plays a key role. This paper proposes that we should from the normative standards of the commercial bank's core business systems for the starting as soon as possible, to organize and work out the Integrated Financial Enterprise (IFE) solution, and its corresponding demonstration project. A complete set of solutions, including the IFE general framework model, the general component library, application-layer communication protocol FL7 in financial industry, the JAVA implementation for IFE core component, system testing standards and the corresponding test asset library are designed and developed. Based on the IFE proposed draft, the commercial bank can accomplish the variance analysis to design core business systems and IT platforms suited to itself and avoid repeated introduction. It can also carry out evaluation and testing to business process and varieties of process reengineering programs, to avoid the risk in the bank’s process changing.
Rewiring for threshold logic circuit minimization

Recently, many works have been focused on synthesis, verification, and testing of threshold circuits due to the rapid development in efficient implementation of threshold logic circuits. To minimize the hardware cost of threshold circuit implementation, this paper proposes a heuristic that consists of rewiring operations and a simplification procedure. Additionally, a subset of input vectors of a gate, called critical-effect vectors, are proved to be complete for formally verifying the equivalence of two threshold logic gates, instead of the whole truth table in this paper. This achievement can accelerate the equivalence checking of two threshold logic gates. The experimental results show that the proposed heuristic can efficiently reduce the cost.