nils-herrmann/clean_scirepeval huggingface dataset:
Segmentation of Lung Region for Chest X-Ray Images Based on Medical Registration And ASM

It's very important to locate and recognize the lung region accurately in chest X-ray images in clinical application and research. This paper provides a novel method to extract the lung region in the chest X-ray images. In this paper, the active shape model (ASM) based on deformed technology is applied to segment the lung region. In order to get a more accurate and time-saving segmentation result, we also improve the original ASM. Firstly, Because of the body location and the individual difference, we use thin-plate spline method to register the chest radiographs in order to get a more appropriate shape model .Secondly, apex pulmonis and gulus costa are localized and used to initialize the mean shape mode, and the mean intensity of the bound rectangle regions of the initial shapes is adopted to match the shapes. Finally, a multi-resolution framework based on Gaussian pyramid was introduced to acquire quick iteration. Experimental results show that our algorithm performs significantly better than the original ASM.
How do Latecomer Firms Capture Value From Disruptive Technologies? A Secondary Business-Model Innovation Perspective

To fill the research gap between extant theory of disruptive technology and latecomer firms' business-model innovation practices in emerging economies, we explore the new latecomer advantage implied in the phenomena of overshooting and nonconsuming. Based on inductive theory building with a comparative case study, we find that latecomer firms, though disadvantaged in technological capabilities and market resources, can successfully introduce disruptive technologies from advanced economies into emerging economies through secondary business-model innovations. They provide cheaper, simpler, but good enough products or services that ordinary citizens in emerging economies can easily afford and access. How do latecomer firms capture value from disruptive technologies within the emerging economies context? While articulating an appropriate value proposition that is attractive for local customers is of great importance for those latecomer firms, they should also fully utilize strategic partners' complementary assets to build a unique value network embedded within local infrastructure. Thus, those latecomer firms tactfully bypass the substantial first-mover advantages and global advantages of multinational incumbents, and leverage their latecomer advantages such as low price and local savvy. Thus, latecomer firms should not ignore those disruptive growth opportunities within the large population of mass customers and nonconsumers in emerging economies.
A New Algorithm for Knowledge Reduction Based on Neighborhood Rough Set

In order to reduce the practical decision system including continuous attributes, a reduction algorithm based on neighborhood granulation is proposed. In this algorithm, a rough set model is used based on neighborhood equivalence, the indiscernibility relation is measured by neighborhood relation, and the universe spaces is approximated by neighborhood information granules. We construct a features selection algorithm of continuous attributes. The experimental results with UCI data set show that neighborhood model can select a few attributes but keep, even improve classification power. Some improvements for a widely used value reduction method are also achieved in this paper. Using this method reduce discrete information system, the complexity of acquired rule knowledge can be reduced effectively in this way.
A research of feature-based image mosaic algorithm

Due to the stitching precision and speed of existing image mosaicing technique, this paper summarizes three common based on Feature matching algorithm: Harris corner detection algorithm, SIFT (Scale Invariant Feature Transform) algorithm, SURF ( Speeded-Up Robust Features ) algorithm. This paper describes the principle of three algorithm respectively. The performance of the algorithm are compared for corresponding points computing time and stitching precision. Harris algorithm's perform of extracting angular points was worst, and lowest effectiveness of finding corresponding points. SIFT presents its stability for for rotation and scale in most situation, although it is slow. SURF is the fastest one with good performance as same as SIFT.
Implantable Subdural Electrode Arrays for Neural Recordings Based on MEMS Technologies

We present implantable subdural electrode arrays (ISEA) for neural recordings based on MEMS technologies. The electrode array is fabricated on a thin polyimide substrate and then encapsulated with biomedical-grade polydimethylsiloxane (PDMS). Then laser ablating with a CO2 laser is applied to expose the electrode sites. Prototypes of 4×4 gold electrode arrays are fabricated with an exposed electrode diameter of 2.3 mm and electrode pitches of 5.0 mm. The conductor paths are 200-µm- wide. After package, the total array area is approximately 40 mm by 55 mm. To verify the functionality of the subdural electrodes, the electrochemical impedance spectroscopy is measured. At 1 kHz, the electrode impedances vary between 1.5 kΩ and 3.5 kΩ, and the phases between -85o and -70o. Meanwhile, the electrical resistance of the conductor paths is also evaluated under mechanical strain, which shows that the electrical resistance increases approximately linearly with the applied mechanical strain until the polyimide layer ruptures at ~10%. All the characteristics above will permit implanting the subdural electrode array to sense ECoG (electrocorticogram) for epilepsy diagnosis and so on.
Contact Temperature Prediction in Three-Phase Gas-Insulated Bus Bars With the Finite-Element Method

Degradation of the electric contact in gas-insulated bus bars (GIBs) is closely related to the overheating phenomenon occurring at the contacts. This paper employs the finite-element method to solve the 3-D coupled eddy current, fluid, and thermal problems in a three-phase GIB. In the model, the contact resistance is measured and described as a resistor between the conductor and the contact. Computational fluid dynamics is used to predict the temperature rise of the components in the GIB. The temperature-dependent thermal properties of SF6 gas and air are considered in the thermal analysis. The developed methodology is implemented on a 126 kV three-phase GIB prototype and validated with the experimental temperature rise results.
A Novel High Precision Voltage Regulator with High PSR

A high precision voltage regulator was proposed. It can work under the supply voltage from 8V to 20V. Compared with conventional regulators, its performances were improved by boosting the feedback loop gain, minimizing the effect of base width modulation and base current error correction. Simulation results showed the line regulation of 33muV/V and load regulation of 30muV/mA was achieved. And by using a cascode current mirror to shield the core circuit from power supply noise, PSR (power supply rejection) of 120dB at low frequency was also achieved
Passive fuel delivery based on hydrophobic porous silicon for micro direct methanol fuel cells

This paper presents a novel micro direct methanol fuel cell (μDMFC) integrated with passive fuel delivery based on a surface tension driving mechanism to achieve power-free fuel supply. Unidirectional methanol-to-water smooth flow is achieved through the hydrophobic capillaries of the porous silicon membrane utilizing the liquid surface tension difference between methanol and water. The prototype is demonstrated to exhibit good fuel utilization and to operate for longer time compared with conventional μDMFCs. As such, it is believed to be more applicable for future μDMFCs.
Quasi-SM controller for SIDO buck converter with self-adaptive free-wheeling current level

A Single-Inductor Dual-Output (SIDO) buck converter with a novel quasi-sliding mode (SM) controller is presented in this paper. Instead of keeping as a constant current, the free-wheeling current level can vary according to the load condition to extend the load range while maintaining high power efficiency and less cross regulation at the same time. To verify the feasibility of the proposed technique, a SIDO buck converter with two regulated output voltages, 1.8V and 3.3V, is designed and fabricated in HEJIAN 0.35 μm CMOS process. Simulation and experiment results show that the cross regulation can be reduced to as low as 0.057 mV/mA when the load changes from 50mA to 100mA.
Cost Estimating of Weapons Development Based on Rough Sets and ANN Learning

There are some difficulties in using Linearity Regression method to predict the cost of MLRS development under the small sample situation. On the basis of the capacity of dealing with the nonlinear of ANN and the learning capacity of Rough Sets (RS), a new cost estimating method combined with RS and neural network is brought forward, which can use the Relative Reduce theory in Rough Sets to learn or mine the knowledge concealed in the samples, then certain elements after reduce is selected as the inputs of neural network, the cost estimating of weapons development is achieved. An example is provided to prove the precision of the new method is higher than that of the gray model.
Cell balancing management for battery pack

This paper presents a cell balancing management for battery pack. It is used for each cell in battery pack to protect pack from damages such as overheating and overvoltage taken by the different performances between different cells, it manages individual cell with CC-CV charging strategy, shunts charge current smoothly to protect every cell preferably; in order to be suitable for different charge conditions, the smooth shunting voltage reference is set by actual battery pack charge current. The design chooses Li-ion battery whose full voltage is 4.2v as the example. And the design is accomplished under the TSMC 0.35um technology.
Accurate camera calibration for hand-held camera in multi-view stereo reconstruction

A flexible and accurate camera calibration algorithm with circles and concentric circle patterns is proposed for hand-held camera for multiview stereo (MVS) application, which can avoid user interaction for feature extraction in traditional planar pattern based camera calibration method. In order to automatically identify the features in image, we encode the circular patterns according to the size of the patterns. The unique code can be used to match features in images and planar patterns. We use three methods to extract the circle centers, e.g. ellipse fitting, perspective correction and circle chord midpoint approximation methods. Both simulation and real image data are utilized to validate the effectiveness of the proposed method. We use this method in multiview stereo(MVS) to reconstruct three visual hull models from image sequences. The experiments demonstrate that the concentric circle patterns have most accurate results.
An enhanced area-efficient on-chip compensation technique for power converters

A novel high area-efficient on-chip lag (PI) compensation technique for power converters is proposed in this paper. This technique combined current-mode with voltage-mode capacitor multiplier. The compensation capacitor can be reduced to smaller than 10% of the conventional one. This proposed technique is successfully verified by a boost converter system with current program mode (CPM) control. The system proves to be stable for loading conditions from 100mA to 300mA. While conventional compensation capacitor and resistor are 30pF and 2.5MΩ, the proposed technique needs only 3pF and 1.2MΩ consuming 8μA extra current.
A preemptive scheduling algorithm for wireless real-time on-demand data broadcast

On-demand broadcast is an attractive data dissemination method for mobile and wireless computing. In this paper, we propose a new online preemptive scheduling algorithm, called PRDS that incorporates the urgency, the data size and the number of pending requests for real-time on-demand broadcast system. Furthermore, we use pyramid preemption to optimize performance and reduce overhead. We have done a series of simulation experiments to evaluate the performance of our algorithm as compared with other previously proposed methods under a range of scenarios. The experimental results show that our algorithm can substantially outperform other algorithms without jeopardizing other performance metrics, such as response time and stretch.
Content Based Image Retrieval by combining color, texture and CENTRIST

This paper presents a novel framework for Content Based Image Retrieval(CBIR), which combines color, texture and spatial structure of image. The proposed method uses color, texture and spatial structure descriptors to form a feature vector. Images are segmented into regions to extract local color, texture and CENTRIST(CENsus Transform hISTogram) features respectively. Multiple-instance learning (MIL) and Diverse Density(DD) are incorporated with regions as instances to find the objective instance. In addition, to denote the whole structure of image better, we perform PCA to CENTRIST features of all images, i.e. spatial Principal component Analysis of Census Transform(spatial PACT). This framework integrates three features to enhance the retrieval performance. Experiments on COREL standard database invalidate the proposed method by comparing with some state-of-the-art methods.
Foreground segmentation of binocular video based on multiple cues

This paper proposes an automatic foreground segmentation algorithm. We develop an energy function that is composed of disparity, color and contrast cues to extract the foreground object accurately. Disparity is calculated to contribute the energy function with simple and effective methods. Background color model is constructed based on Gmms algorithm. Continuity characteristic is used to get the contrast cue. A binary label is assigned to each pixel by minimizing the designed energy function. A boundary smooth filter in frequency domain is presented in order to paste the foreground object into the virtual background seamlessly. Experimental results demonstrated that our segmentation algorithm based on multiple cues is efficient.
Identification of interpretable and precise fuzzy systems based on Pareto Multi-objective Cooperative Co-evolutionary Algorithm

A novel approach to construct a set of interpretable and precise fuzzy systems based on the Pareto multi-objective cooperative co-evolutionary algorithm (PMOCCA) is proposed in this paper. First, feature selection is used to reduce the dimensionality of the data in order to both improve the performance and reduce computational effort. Then the fuzzy clustering algorithm is employed to identify the initial fuzzy system. Third, the PMOCCA is carried out to evolve the initial fuzzy system to optimize the number of rules, the antecedents of the rules and the parameters of the antecedents simultaneously. In this step, the interpretability-driven simplification techniques are used iteratively to reduce the fuzzy systems, thus the interpretability of the fuzzy systems is improved. Finally, the proposed approach is applied to several benchmark problems, and the results show its validity.
Key technologies and development prospect of urban energy internet

With the expanding of city scale, the energy have become increasingly strained. The outbreak of energy crisis and environmental problem restricts the development of the city seriously, and urban energy internet emerges as the times require. It combines the energy in the city (such as coal, oil, natural gas, wind, solar, electricity and other kinds of energy) through the efficient conversion of energy and the close integration of power grid network, traffic network and natural gas network. From the point of the urban energy development, electricity-centric city energy internet model is built. From the point of planning and operating, the key technologies for urban energy internet construction including active distribution grid, clean energy and electric power replacement, business model innovation are put forward. The development direction of turban energy internet is proposed, which can provide some ideas for energy transformation of the city.
Structure Maintaining Discriminant Maps (SMDM) for Grassmann Manifold Dimensionality Reduction with Applications to the Image Set Classification

For the image-set based classification, a considerable advance has been made by representing original image sets on Grassmann manifold. In order to extend the advantages of the Euclidean based dimensionality reduction methods to the Grassmann Manifold, several methods have been suggested recently to jointly perform dimensionality reduction and metric learning on Grassmann manifold and they have achieved good results in some computer vision tasks. Nevertheless, when handling the classification tasks on the complicated datasets, the learned features do not exhibit enough discriminatory ability and the data distribution of the resulted Grassmann manifold also be ignored which may lead to overfitting. To overcome the two problems, we propose a new method named Structure Maintaining Discriminant Maps (SMDM) for manifold dimensionality reduction problems. As to SMDM, we mainly design a new discriminant function for metric learning. We make experiments on two tasks: face recognition and object categorization to evaluate the proposed method, the achieved better results compared with the state-of-the-art methods, showing the feasibility and effectiveness of the proposed algorithm.
A Dynamic Pattern Classifier for Complex Information Systems Based on Fuzzy Petri Nets

Aiming at the problem of pattern recognition for the complex information systems with many dynamic fault types, a dynamic pattern classifier is constructed based on fuzzy Petri nets for the fault classification of complex information systems. fuzzy Petri net is a machine-learning algorithm that has been successfully used in pattern recognition for cluster analysis. In this paper, the dynamic recognition classifier is constructed based on two types of learning. The static aspect of the learning is ensured by classifiers or systems of classifiers, while the dynamic aspect is translated by the learning of the planning of the various states by fuzzy Petri nets. Finally, the practical recognition process is given in an illustrative example of a synthetic data set. The results show that the method is effective and reasonable through approving.
Single Amplifier Sigma Delta Modulator With Input Feedforward

It has recently become very popular to feedforward the input signal in wideband delta-sigma modulators, so that the integrators only process quantization errors. The advantage is that the actual signal is not distorted by op-amp and integrator nonlinearities. The paper applies this technique to single amplifier based sigma delta modulator in both analog and digital way. Their characteristic has been analyzed and simulation results are given which show the improved performance
Skills evaluation of junior-level software talents

many software industrial association established in all provincial capital cities in China. Providing service for talents and company is their main responsibilities. The evaluation of software talents skills is multi-factor, multi-objective and multi-criteria decision-making problems. The current selection and evaluation method which the company used commonly is single, non-objective, non-universal and low input-output ratio. Base on a lot of investigation and research in typical software industry, the method of AHP (analytic hierarchy process) and FCEM (fuzzy comprehensive evaluation method) are used to evaluate the skills level of junior-level software talents. It makes evaluation results more objective and reasonable.
Application of tunnel monitoring measurement information feedback in surrounding rock dynamic classification

The subsurface engineering of highway (railway) tunnels etc are concealed work, because of the effects of environment and outlay, it is impossible to prospect accurately, and rock and soil is complex and multivariable, thus master project planning and scheduling document and lining structural design, that is preliminary design, are blindfold. Accordingly NATM has been widely used in the underground works, through the site monitoring and measurement alters design, adjusts supporting parameters and construction technique, so insure the tunnel construction's safety. The key of monitoring measurement Information feedback design is determination the level of the surrounding rock, which was judged mainly based on engineering experience in the past. According to the monitoring and measurement information of perimeteral convergence, surrounding rock pressure and internal force of parapet grille reinforcement in some highway tunnel, through statistical regression analysis on masses of monitoring data, acquires the statistical average of monitoring data when surrounding rock go to stable condition, which can be used for the basis of surrounding rock dynamic classification; adopt entropy method; give the calculation procedures of surrounding rock dynamic classification; according to the monitoring results of some tunnel, give a example to show the quantitative method of surrounding rock classification is feasible and accurate; which provides a new meaning and theoretical basis for the NATM.
An extended OOPN model and its specification based on PNML

PNML is a universal interchange format that can be used to represent most kinds of Petri nets. This paper proposed an extended object-oriented Petri net (EOOPN) model for modeling and reliability analysis of complex systems. In order to represent this model using PNML, the original markup language should be extended with new labels to define new type of Petri net, which appeared in EOOPN. This paper showed the extension to label transitions, arcs and places by XML syntax. Finally, an example is given to show the modeling of EOOPN with PNML. With PNML representation, the EOOPN model could easily adapt to various tools supporting.
A reconfigurable seamless-transition DC-DC Converter With lossless current-sensing technique

A reconfigurable DC-DC converter that provides both step-down and step-up functions is proposed to ensure a stable output voltage over full voltage range of battery in portable devices. The converter can automatically adjust itself into different modes, so as to enhance system efficiency. Moreover, seamless transition is achieved to reduce output ripple and improve line regulation. Additionally, a novel current-sensing technique is applied to realize lossless and accurate current sensing. The controller IC is designed and fabricated in 1.5-um BCD process. Test results verified the design target.
An adaptive on-time controlled boost LED driver with high dimming ratio

A high efficiency high dimming ratio boost LED driver without color shift issue is presented in this paper. The boost converter is controlled by an adaptive on-time (AOT) controller, using improved control stratagem based on constant on-time (COT) control, so as to realize fast transient response to achieve high dimming ratio. With AOT control, the operation frequency maintains constant under continuous current mode (CCM) and the output voltage ripple could be reduced. In addition, Pulse frequency modulation (PFM) is implemented automatically under discontinuous current mode (DCM) to improve the system efficiency. The chip was designed and implemented in 1.5 μm bipolar-CMOS-DMOS (BCD) process. The simulation and experimental results showed that the minimum dimming on-time could be reduced to 1.2 μs, which means a dimming ratio of 2000:1 is available at dimming frequency of 400 Hz.