nils-herrmann/clean_scirepeval huggingface dataset:
The Remote Monitor and Control System for Motion Controller Based on the Ethernet

In order to realize remote debugging and diagnostics to the motion controller with Ethernet, equipment with SIMOTION motion control system is applied. The application of virtual private network technology, combined with SCOUT tool, a remote control through Internet is used. This paper introduces the hardware and software of the motion control system, the structure and composition of remote control system and the core technology for specific steps. A detailed analysis of the application effect is shown. The result shows that the remote monitor and control system not only reduces system downtime and maintenance response time, but also saves a lot of human and material resources. The application shows the feasibility of remote debugging and diagnostics to motion controller using VPN technology.
Tri-mode stub-loaded ring resonator and its application to miniaturized wideband bandpass filter design

Stub-loaded ring resonator and its application to miniaturized wideband BPF are presented in the paper. Detail analysis shows that the presented stub-loaded ring resonator can allocate three resonant modes within the desired frequency range, and due to the dual path effect of the ring resonator, two transmission zeros can be introduced at the upper and lower cut-off frequencies, resulting high passband selectivity. A tri-mode wideband BPF is simulated, fabricated, and measured. The EM simulated and measured results are presented, and an excellent agreement is obtained.
Semiautomatic Segmentation of CT Cardiac Images

In order to complete the semi auto-segmentation of dual-source CT image of heart and extract the structure of heart accurately, propose a novel segmentation method of CT images based on graph cuts based active contour and anisotropies spreads algorithms. The method combines image characteristics with high-level segmentation model, Due to effectively used heart anatomical structure, the noise and fuzzy boundary have less effect on the segmentation results. It is used to quickly and accurately segment ventricle, atria and coronary artery. After the pre-segmentation of the DSCT images, the ventricular and atrium are fast and accurately segmented through minimize the energy function. The segmentation method can effectively process cardiac medical image of DSCT. It provides new methods for clinical doctors to get more information of cardiac images.
Continuous k-Nearest Neighbor Queries on Multi-core CPUs

Modern microprocessors employ multi cores to accelerate computations, and parallelizing multiple queries execution to exploit multi-core parallelism has become a challenge for moving objects database applications. To evaluate massive concurrent queries towards moving objects, parallel processing techniques and cache-conscious algorithms adapting to memory hierarchy and multi-core architecture should be developed to maximize the processor computation abilities and reduce the memory stalls. This paper first presents a distributed moving objects updating strategy to reduce the computing cost of server. Further, in-memory spatial network adjacent matrix, shortest path matrix and hash table structures are introduced to describe the road network topology and store the moving objects. A shortest path based network expansion (SPNE) algorithm is proposed to decrease the processing cost of k nearest neighbor (k-NN) queries by reducing the search network space. For massive k-NN queries evaluation, we use a multi-staged engine, which exploits pipeline strategy and departs the query processing into three simultaneous stages to improve the parallelism with multi-threaded technology. And we present a multi-threaded network expansion (MSPNE) algorithm for massive k-NN queries processing. MSPNE algorithm uses threaded workload parallelism and cache-conscious execution reorganization strategies to improve the spatial and temporal locality. Experimental evaluation on a dual-core platform and analysis show that SPNE and MSPNE algorithms achieve a performance improvement over the existing traditional optimization counterparts.
Real-Time Near-Duplicate Elimination for Web Video Search With Content and Context

With the exponential growth of social media, there exist huge numbers of near-duplicate web videos, ranging from simple formatting to complex mixture of different editing effects. In addition to the abundant video content, the social Web provides rich sets of context information associated with web videos, such as thumbnail image, time duration and so on. At the same time, the popularity of Web 2.0 demands for timely response to user queries. To balance the speed and accuracy aspects, in this paper, we combine the contextual information from time duration, number of views, and thumbnail images with the content analysis derived from color and local points to achieve real-time near-duplicate elimination. The results of 24 popular queries retrieved from YouTube show that the proposed approach integrating content and context can reach real-time novelty re-ranking of web videos with extremely high efficiency, where the majority of duplicates can be rapidly detected and removed from the top rankings. The speedup of the proposed approach can reach 164 times faster than the effective hierarchical method proposed in , with just a slight loss of performance.
Symmetric deflation based multiview canonical correlation learning algorithm

Multiview canonical correlation analysis (MCCA) is an effective tool for analyzing the relationships among group- aligned multidimensional samples, which has been applied to the fields of pattern recognition and computer vision. In MCCA, its first-stage canonical variables are solved by a multivariate eigenvalue problem that can be computed by Horst method. However, how to use the algorithm for effectively and stably solving higherstage projection directions is now still not clear. In this paper, we propose a multiview canonical variates learning algorithm, which uses a symmetric deflation strategy instead of asymmetric one for multi-set solutions. Also, we prove the convergence property of the proposed algorithm. Clearly this benefits the theoretical development of MCCA and can facilitate its applications in practice.
Biomarker Discovery from Proteomic Data Based on Wavelet Package Transform and AdaBoost

Advances in proteomics provide a new method for early detection of cancer, it can provide a wealth of information and rapidly generate large quantities of data from the analysis of biological specimens. In particular, proteomic pattern of body fluid has attracted attention as an approach to early detection of cancer. Mass spectrometry can provide rapid and precise measurements of the proteins in the body fluid. But the data processing is still a challenge due to noise artifact and high dimensionality of the proteomic data. In this paper, we proposed a scheme that combined wavelet package transform, statistic analysis and AdaBoost to process a public prostate cancer proteomic dataset, the obtained discriminative proteomic pattern can differentiate the cancer form control with high sensitivity and specificity.
Numerical Simulation of Injection and Mixing on New Power Systems of Liquid-Fueled Pulse Detonation Engines

Pulse detonation engines (PDE) have received significant attention in recent years due to their potential advantages over conventional propulsion systems, including high thermodynamics cycle efficiency, high thrust/weight and specific impulse. The fuel-air injection and mixing is one of important research issues. To optimize the fuel-air injection and mixing, a three-dimensional computation model of PDE is established. The mixing process for twin-fluid air-assisted atomizers for air supplied with tangential inlet and axial inlet is numerically analyzed by using the software Fluent as platform to solve the three-dimensional Navier-Stokes equations, and the relationship between drop size and air mass and gasoline/air ratio is arrived at. Numeric simulations results of the process with different inlets show that well mixing but bad concentration distribution occurs with tangential inlet especially along the central axis which makes PDE difficult to work in high frequency, while bad mixing but well-proportioned distribution occurs through axial inlet. And with the same gasoline/air ratio initialized, as the air mass increases, the droplet size decreases, and the result is also correct for the case in which the gasoline/air ratio decreases with the same air mass initialized. These results are found to be in good agreement with the limited experimental data that is currently available. The conclusions are available for the design of liquid-fueled PDE.
Dynamic Bayesian Evaluation Method for System Reliability Growth Based on In-Time Correction

Small sample size is often a limitation in reliability field tests for expensive, destructive systems, which makes it difficult to evaluate system reliability accurately. A new system's development process is usually comprised of several stages within which the system experiences design updating, and prototype field testing, thus enabling the improvement of system reliability. A dynamic Bayesian evaluation method is presented in this paper to cope with this problem for binomial systems whose reliability grows along with stages of the system's development process. To implement the proposed method, a new discrete reliability growth model, derived from the learning-curve property, is introduced to describe system reliability growth through the stages of the development process, which is also capable of predicting the reliability at the next stage, based on the data acquired in the current stage. The prediction result, which acts as the prior knowledge for reliability at the next stage, is applied to determine the prior distribution of system reliability through the Maximum Entropy method. The advantage of the method is that it employs field test data from various stages of the system's development process to evaluate system reliability, comparing with traditional methods. The given example demonstrates that the proposed method adapts to evaluate system reliability when the sample size of the field test is small.
A centralized power allocation algorithm for TDD CDMA system with smart antenna in a multi-cellular environment

In TDD CDMA systems, uplink and downlink work at the same frequency, which causes interference between them. The interference in uplink and downlink connection is analyzed respectively within a time slot. Adaptive beamforming is used in uplink receiver beamforming. Beam-steering method is used to achieve downlink transmission beamforming for complexity in downlink. From the beamforming gain and path gain between transmitter and receiver, a normalized gain matrix is constructed. With this matrix we propose an optimum power allocation method that satisfies all users' SIR requirements at the minimum transmission power. The power allocation algorithm provides an estimate of the optimum performance of all power control schemes
Diversified Visual Attention Networks for Fine-Grained Object Classification

Fine-grained object classification attracts increasing attention in multimedia applications. However, it is a quite challenging problem due to the subtle interclass difference and large intraclass variation. Recently, visual attention models have been applied to automatically localize the discriminative regions of an image for better capturing critical difference, which have demonstrated promising performance. Unfortunately, without consideration of the diversity in attention process, most of existing attention models perform poorly in classifying fine-grained objects. In this paper, we propose a diversified visual attention network (DVAN) to address the problem of fine-grained object classification, which substantially relieves the dependency on strongly supervised information for learning to localize discriminative regions com-pared with attention-less models. More importantly, DVAN explicitly pursues the diversity of attention and is able to gather discriminative information to the maximal extent. Multiple attention canvases are generated to extract convolutional features for attention. An LSTM recurrent unit is employed to learn the attentiveness and discrimination of attention canvases. The proposed DVAN has the ability to attend the object from coarse to fine granularity, and a dynamic internal representation for classification is built up by incrementally combining the information from different locations and scales of the image. Extensive experiments conducted on CUB-2011, Stanford Dogs, and Stanford Cars datasets have demonstrated that the pro-posed DVAN achieves competitive performance compared to the state-of-the-art approaches, without using any prior knowledge, user interaction, or external resource in training and testing.
A Task-Grouped Approach for the Multi-robot Task Allocation of Warehouse System

Multi-Robot System can achieve intelligent warehouse management, one of its key issues is the rational allocation and coordination of tasks. According to the characteristics of tasks in warehouse, a new task-grouped approach is proposed, which is based on the idea of "the global tasks are grouped first and redistributed later ", improving the traditional static allocation algorithm is applied to the dynamic task in warehouse, where dynamic tasks can be processed in the form of relatively static. The method that gets near optimal assignments enhances the flexibility of the algorithm. Finally, the algorithm is simulated and the experimental results show that the method reduces the total time of the global task allocation, improves the performance of the global task path equilibrium, and the complexity is lower.
Intelligent residential security alarm and remote control system based on single chip computer

This paper presents intelligent residential burglar alarm, emergency alarm, fire alarm, toxic gas leakage remote automatic sound alarm and remote control system, which is based on 89C51 single chip computer. The system can be automatic alarm, automatic calling the police hotline number. It can be used voice alarm and show alarm occurred address. It can set up and modify user password. It can be recordable and voice suggestion. It can be used telephone remote control electrical power. The alarm system has design innovation, multiply function, low cost, high reliability and so on.
Mission reliability analysis of missile defense system based on DODAF and Bayesian networks

A method combining Department of Defense Architecture Framework (DODAF) 2.0 and Bayesian networks (BN) is introduced. This method can not only provide qualitative description for large systems using DODAF models, but also analyze mission reliability quantitatively based on Bayesian network. The steps are given to build DODAF-BN models and a missile defense system (MDS) DODAF-BN model is built. Furthermore, the mission reliability of MDS is calculated and analyzed in different scenarios.
A cosine similarity-based compensation strategy for RSS detection variance in indoor localization

Currently, all kinds of Location-based Services (LBS) are gradually demanding more location information of mobile users. The WiFi-based indoor positioning technologies have been investigated intensely. However, both positioning accuracy and stability are often degraded by RSS detection variance between different devices. To solve the two problems, this paper proposes a compensation strategy for RSS detection variance based on cosine similarity. Specifically, it uses cosine similarity as the metric to determine whether different devices could conduct compensation for RSS variance. To obtain the device pairs that satisfy the metric standard, it uses ratio correction method to compensate for RSS detection variance thus effectively solves the positioning accuracy and stability reduction causing by RSS detection variance between different devices. Experiments have shown that the proposed strategy reduces positioning error and improves positioning stability significantly.
A Novel Method for Detecting New Overlapping Community in Complex Evolving Networks

It is an important challenge to detect an overlapping community and its evolving tendency in a complex network. To our best knowledge, there is no such an overlapping community detection method that exhibits high normalized mutual information (NMI) and  ${F}$ -score, and can also predict an overlapping community’s future considering node evolution, activeness, and multiscaling. This paper presents a novel method based on node vitality, an extension of node fitness for modeling network evolution constrained by multiscaling and preferential attachment. First, according to a node’s dynamics such as link creation and destruction, we find node vitality by comparing consecutive network snapshots. Then, we combine it with the fitness function to obtain a new objective function. Next, by optimizing the objective function, we expand maximal cliques, reassign overlapping nodes, and find the overlapping community that matches not only the current network but also the future version of the network. Through experiments, we show that its NMI and  ${F}$ -score exceed those of the state-of-the-art methods under diverse conditions of overlaps and connection densities. We also validate the effectiveness of node vitality for modeling a node’s evolution. Finally, we show how to detect an overlapping community in a real-world evolving network.
Allied fuzzy c-means clustering using kernel methods

Allied fuzzy c-means (AFCM) clustering is a hybrid fuzzy clustering algorithm based on the combination of fuzzy c-means (FCM) and new possibilistic c-means (NPCM). AFCM can deal with noisy data better than FCM and does not generate coincident clusters. With kernel methods AFCM is improved as its kernel learning machine model. This proposed algorithm is called kernel allied fuzzy c-means (KAFCM) clustering. KAFCM is suitable for classification of nonlinear separable patterns while AFCM deals with linear separable patterns well. KAFCM can nonlinearly map the input data into a high-dimensional feature space where the nonlinear pattern now appears linear and AFCM is performed. The better performance of our proposed algorithm is shown by performing experiments on artificial dataset and standard IRIS dataset.
Stereoscopic image quality assessment via convolutional neural networks

This paper mainly introduces an image quality assessment for stereoscopic images via Convolutional Neural Networks (CNN). Firstly, the left and the right view images of a stereoscopic image need to be fused in the way of Principal Component Analysis (PCA). Secondly, method of Mean Subtraction and Contrast Normalization (MSCN) is applied in the fusion images. Finally, taking non-overlapping small patches of each image as the input, the CNN can train an evaluation model between image features and Different Mean Opinion Scores (DMOS). The model can predict quality scores of image patches and we average the patch scores of each image to get the final predicted quality scores of large images.
Self-adaptive window control technique for hysteretic buck converter with constant frequency

A hysteretic buck converter with self-adaptive window controller is presented in this paper. Since the switching frequency of conventioal hysteretic buck converter varies with the input voltage and load current, which might result in severe Electro-Magnetic Interference (EMI), a self-adaptive window control technique is proposed to tackle this problem. The proposed converter is design and fabricated in TSMC 0.35 μm CMOS process. Simulation and experiment results show that the switching frequency of this converter is held a near constant value when input voltage or load current changes.
A sensitivity-enhanced electric field sensor with electrostatic field bias

An electrostatic field biased cantilever electric field sensor with sensitivity improvement is proposed in this paper. Theoretically, the sensitivity of the sensor is proportional to the applied electrostatic field strength. A demonstrated sensor is developed, which consists of a Silicon cantilever coated with an Aluminum Nitride (AlN) piezoelectric film for signal readout, and a PTFE electret layer for electrostatic field bias. When the surface potential of the electret layer is −683 V, the sensitivity of the cantilever electric field sensor reaches 15.8 mV/(kV/m), and the resolution is about 20 V/m.
Application of utility management based on article usage

This paper proposes utility management concept and two standard formulas in library management as application. Consumer preferences made articles to be usefulness and actual data of the effective usage were analyzed the level of spreading article usefulness. Utility function may help articles to be utility and evaluate in current management. The utility management for using the possessive articles in the utility value as the premise of the management, with the effective usage of empirical data to analyze the usefulness of continuing degree of these, and to dug the potential usefulness, to make full use of these, the maximum of use impact and management effectiveness. After goods get out the economy constituent, the action of goods gives expression in the process of utility value in utility management. The book usage factor can evaluate a kind of books within same category and the book usage half-life can evaluate all kinds of books within same category. Two formulas can evaluate different category books in the library collection.
pH-responsive drug-delivery devices for implantable applications

A drug-delivery microdevice integrating pH-responsive nano-hydrogel composite membranes functioning as intelligent nano valves is described. The polymeric microdevices are monolithic without requiring peripheral control hardware or additional components for controlling drug-release rates. pH-responsive nanoparticles were synthesized and embedded into a composite membrane. The resulting pH-responsive composite membrane was integrated with PDMS micro reservoirs to form the proof-of-concept microdevices. In vitro release characterization of the microdevices was conducted in which the release rate of Vitamin B12 (VB12) as a model drug increased dramatically when the local pH value was decreased from 7.4 to 4. By adjusting nanoparticle percentages, drug reservoir shape and size, and drug loading concentrations, complex drug release profiles in response to local pH changes can be achieved, functioning as a platform technology for intelligent drug delivery.
Tacit Knowledge Explicitation in Knowledge Management and Technologic Innovation Performance: An Empirical Study

Based on sample data of some chinese enterprises in knowledge-intensive industry, we use structure equation modeling (SEM) to analyze how the information resource, system structure, situational system factors affect the tacit knowledge explicitation and subsequently enterprise's technologic innovation performance. We find: information resource, system structure, situational system factors have significantly positive impact on tacit knowledge explicitation; System structure factor has indirect impact on technologic innovation performance working through tacit knowledge explicitation, and information resource factor is distinctly correlated to situational system factor, presenting significant synergistic effect between them; Information resource factor, situational system factor, tacit knowledge explicitation have significantly positive impact on technologic innovation performance. Finally, we present research conclusion and limitation, moreover put forwards some proposals to promote tacit knowledge explicitation so as to improving technologic innovation performance of enterprise.