nils-herrmann/clean_scirepeval huggingface dataset:
An evolutionary multi-objective scenario-based approach for Stochastic Resource Investment Project Scheduling

Many planning problems, such as mission capability planning, can be modelled as project scheduling problems. Unlike conventional deterministic project scheduling problems, project scheduling problems involve uncertainty and the execution of the plan is very likely to be perturbed by many factors. In other words, the circumstances under which the plan will be executed are changing and stochastic. In this paper, we first use scenarios to represent the stochastic elements in the problem; these are: perturbation strength and perturbation occurrence time. We define and explain the Stochastic Resource Investment Project Scheduling (SRIPS) problem. A multi-objective optimization model of SRIPS is proposed where three optimization objectives are considered simultaneously: makespan, cost, and robustness. A multi-objective genetic algorithm is employed to solve the problem. Finally, we generate two test problems with 30 and 60 non-dummy activities to validate the performance of the proposed approach and analyze the sensitivity of the results to different parameter settings.
Detection of VPC using wavelet transform and fuzzy neural network

A combination of fuzzy neural network (FNN) and wavelet transform (WT) is proposed to identify the ventricular premature contraction (VPC) beats from the Holter ECG. First, the Holter ECG is transformed into different scales using a quadratic spline wavelet. Three features, including the widths of the QRS complex at scales 2/sup 3/ and 2/sup 4/ and the area of QRS at scale 2/sup 5/, are extracted and input into a four-layer FNN. The Gaussian function is adapted as the membership function in the FNN. In addition, during the training period, the backpropagation algorithm is used to train this FNN. The preliminary results indicate that the proposed method can achieve high accuracy in the detection of VPC.
Implementation of Reconfigurable Patch Antennas Using Reed Switches

Implementing a reconfigurable patch antenna using reed switches avoids the need to place bias and control lines in the immediate vicinity of the radiating elements, and thereby avoids the attendant impacts on the radiation performance of the antenna. An example of such an antenna comprises a pair of hexagonal patches and a reed switch that connects the two patches at their adjacent vertices. Energizing a coil that either closes or latches the reed switch will shift the resonant frequency of the antenna. Because the coil and associated electronics are located beneath the ground plane, they do not materially affect the radiation performance of the antenna. The reed switches have the potential to be used in full-band and full-polarization antennas, and design concepts are given.
Using DBSCAN clustering algorithm in spam identifying

In the field of internet research, anti-spam mechanism has become a focus currently. The identification of spam plays an important role in current anti-spam mechanism. In order to identify spam efficiently, it usually needs to be able to identify similar emails, i.e. spam clustering. Using the present methods to cluster the emails, many similar emails will be clustered into several groups. For improving the accuracy of spam identification, we present a new clustering method which is based on the DBSCAN clustering algorithm and nilsimsa digest algorithm. Using this method, all emails identified similar artificially are clustered together. The result of the simulation shows that the clustering method based on DBSCAN and nilsimsa performs with higher clustering accuracy than the other clustering methods. From the simulation result, we can also conclude that the shape of the spam digest subspace is irregular.
Learning Maximum Margin Temporal Warping for Action Recognition

Temporal misalignment and duration variation in video actions largely influence the performance of action recognition, but it is very difficult to specify effective temporal alignment on action sequences. To address this challenge, this paper proposes a novel discriminative learning-based temporal alignment method, called maximum margin temporal warping (MMTW), to align two action sequences and measure their matching score. Based on the latent structure SVM formulation, the proposed MMTW method is able to learn a phantom action template to represent an action class for maximum discrimination against other classes. The recognition of this action class is based on the associated learned alignment of the input action. Extensive experiments on five benchmark datasets have demonstrated that this MMTW model is able to significantly promote the accuracy and robustness of action recognition under temporal misalignment and variations.
Multi-scale visual tracking by sequential belief propagation

A novel statistical method is proposed in this paper to overcome abrupt motion for robust visual tracking. Existing tracking methods that are based on the small motion assumption are vulnerable to abrupt motion, which may be induced by various factors, such as the unexpected dynamics changes of the target, frame dropping and camera motion, etc. Although with computational benefits, methods based on hierarchical search is inadequate to this problem because the propagation of the searching error may end up with bad estimates in fine scales. Since different scales contain different salient image features, we propose a new formulation in which searching and matching will be done collaboratively in different scales. The theoretical foundation of this new approach is based on dynamic Markov networks, where the bi-directional propagation of the belief of the target's posteriors on different scales reveals the collaboration among them. A nonparametric sequential belief propagation algorithm for the dynamic Markov network is developed by implementing the collaboration of a set of particle filters. Extensive experiments have demonstrated the effectiveness and efficiency of the proposed method to cope with various types of abrupt motions.
Optimized road network emergency restoration scheduling for relief distribution

In order to minimize the length of time required for emergency relief distribution, the decision-maker always considers damaged road network restoration as a strategy. Focusing on the interrelationship between relief distribution and road network restoration, we provide a transparent and feasible solution for solving optimized road network emergency restoration scheduling for relief distribution. Combining the road network emergency restoration and relief distribution, a combinatorial optimization model is developed in this paper. Consequently, a newly developed genetic algorithm is designed to solve this problem. A case study is presented to highlight the significance of the proposed model as well as the efficacy of solution strategy.
A unified approach to salient object detection via low rank matrix recovery

Salient object detection is not a pure low-level, bottom-up process. Higher-level knowledge is important even for task-independent image saliency. We propose a unified model to incorporate traditional low-level features with higher-level guidance to detect salient objects. In our model, an image is represented as a low-rank matrix plus sparse noises in a certain feature space, where the non-salient regions (or background) can be explained by the low-rank matrix, and the salient regions are indicated by the sparse noises. To ensure the validity of this model, a linear transform for the feature space is introduced and needs to be learned. Given an image, its low-level saliency is then extracted by identifying those sparse noises when recovering the low-rank matrix. Furthermore, higher-level knowledge is fused to compose a prior map, and is treated as a prior term in the objective function to improve the performance. Extensive experiments show that our model can comfortably achieves comparable performance to the existing methods even without the help from high-level knowledge. The integration of top-down priors further improves the performance and achieves the state-of-the-art. Moreover, the proposed model can be considered as a prototype framework not only for general salient object detection, but also for potential task-dependent saliency applications.
A 0.13µm CMOS ΔΣ PLL FM transmitter

A short range FM transmitter is presented. It uses an architecture where the output frequency of a phase locked loop (PLL) is modulated by varying the division number of the feedback divider, using the 1-bit output of a ΔΣ ADC. The measured total harmonic distortion (THD) plus noise is less than 1% at 75 kHz deviation. The transmitter is fully integrated in a 0.13µm CMOS process and the core area is 0.24 mm2. The current consumption is 4.4mA from a 1.2V supply.
Research on Stock Network Modeling and Risk Propagation Based on Complex Network Analysis

The price fluctuation in the stock market is often accompanied by risks, the spread of which affects the entire financial system and even the stable operation of the social economy as a whole. Considering the relatively mature research for the network characteristics of the stock market, this paper attempts to employ the complex networks analysis method to study the risk of the stock market. We firstly analyze the determinants of the stock influence (SI), put forward the measurement method of SI and thus establish the risk propagation model. On that basis, we propose GASI, the influence maximization algorithm for stock network, and dig out the key nodes that maximize SI. Experimental results show that price fluctuations of a small number of stocks may largely impact the other stocks, even the entire stock market. These influential stocks distribute in almost all industries, but are mainly in finance, manufacturing and mining. In addition, compared to the PageRank algorithm, the GASI algorithm can influence more nodes and widen the range of risk propagation, and effectively identify key stock nodes with greater risk influence from the stock network.
The study of guided emergent behavior in system of systems requirement analysis

Emergent behavior is a core trait of SoSs. But the emergence cannot be predicted before its appearance. We propose a method which using Semi-autonomous Agent based simulation to detect the emergent behaviors of SoSs. Agent based Modeling and Simulation are available for the observation of emergent behaviors of SoSs. We propose the multi-agent based simulation methods to demonstrate the evolution of SoSs. Semi-autonomous Agents is applied to model the elements which are consists of SoSs. The framework of Parallel executive and feedback to real world is used to verify and validate the emergent properties which are useful to the SoSs or not. Through the comparation between real world executive results and simulation outcomes, we could regulate the parameters or adjust rules running in the SoSs and guide the emergent behavior of SoSs to the stakeholder's desire direction.
Performance evaluation on high frame-rate speckle tracking

Doppler ultrasound for quantifying blood and tissue motion is angle-dependent and has maximum detectable velocity limitation. Speckle tracking, on the other hand, can be utilized to estimate velocity vectors. The combination of high frame-rate imaging and speckle tracking has the potential to achieve accurate motion estimation but the computation requirement may be a concern. It is the purpose of this study to assess performance of the combination of high frame-rate data acquisition and speckle tracking, called high frame-rate speckle tracking. Main factors under investigation include image signal-to-noise ratio (SNR) and velocity gradients. Two sets of experiments and one set of simulations of bulk motion and flow motion were conducted to evaluate the influences of the factors mentioned above. The results show that high accuracy can be achieved under sufficiently high SNRs. In addition, computational complexity of high frame-rate speckle tracking is acceptable due to the smaller search range resulting from the relative smaller displacement under high frame rate imaging.
Unifying Spatial and Attribute Selection for Distracter-Resilient Tracking

Visual distracters are detrimental and generally very difficult to handle in target tracking, because they generate false positive candidates for target matching. The resilience of region-based matching to the distracters depends not only on the matching metric, but also on the characteristics of the target region to be matched. The two tasks, i.e., learning the best metric and selecting the distracter-resilient target regions, actually correspond to the attribute selection and spatial selection processes in the human visual perception. This paper presents an initial attempt to unify the modeling of these two tasks for an effective solution, based on the introduction of a new quantity called Soft Visual Margin. As a function of both matching metric and spatial location, it measures the discrimination between the target and its spatial distracters, and characterizes the reliability of matching. Different from other formulations of margin, this new quantity is analytical and is insensitive to noisy data. This paper presents a novel method to jointly determine the best spatial location and the optimal metric. Based on that, a solid distracter-resilient region tracker is designed, and its effectiveness is validated and demonstrated through extensive experiments.
Induction-motor stator and rotor winding temperature estimation using signal injection method

A new method to estimate the induction-motor stator and rotor resistance (R<sub>s</sub> and R<sub>r</sub>) and temperature (T<sub>s </sub> and T<sub>r</sub>) was proposed in this paper. Two signals with proper frequencies that yield high sensitivities of the terminal measurement to R<sub>s</sub> and R<sub>r</sub> at a given rotor speed are injected to the induction machine to estimate R<sub>s</sub> and R <sub>r</sub>. The estimated R<sub>s</sub> and R<sub>r</sub> are then used to estimate T<sub>s</sub> and T<sub>r</sub>, respectively, according to the relationships between R<sub>s</sub> and T<sub>s</sub>, and R<sub>r</sub> and T<sub>r</sub>. Simulation and experimental work have been carried out to verify the proposed method. The proposed method can estimate both stator and rotor resistance and temperature with high accuracy, and the estimated R<sub>s</sub> and R<sub>r</sub> can be updated in real time. It does not require an additional hardware or a large amount of finite-element analysis (FEA) or experimental data for implementation
Modeling of heavy metals behavior based on fly ash from MSWI in a swirling furnace

Based on the experimental results and existent states of heavy metals in fly ashes and slag, the gas-solid reaction theory during the melting process, heat and mass transfer of particles in fly ashes, chemical dynamics theory such as volatile heavy metals diffuseness, the plentiful experimental datum were analyzed. Theoretical simulated results from the predicted model are basically consistent with experimental data concerning Co, Cu, Ni, Cr, and Mn fixed rates from in swirling furnace. Results indicate that this approach permits to predict the extent of heavy metals vaporization from a glassy matrix, such as As, Cd, Pb, Zn, and Hg, when heavy metals in swirling melting. The model of heavy metals melting reaction and the volatility in particles during fly ashes melting process has been constructed.
Occlusion pattern-based dictionary for robust face recognition

Robust sparse representation has been applied to tackle some challenging problems in face recognition. In this paper, we propose a new method called occlusion pattern based sparse representation classification (OPSRC). First we find the contiguous occlusion area in the query image to create an occlusion pattern. Then, we add the occlusion pattern to all face images in the face image dictionary, resulting in an occlusion dictionary. The original dictionary and occlusion dictionary are solved together. By assuming some frequently occluded parts of the face and using additive form of half-quadratic optimization that has good performance of error detection, we create occlusion patterns and construct a dictionary robust to occlusion, especially in the case of per sample of each person. Our proposed method OPSRC outperforms other benchmark methods, revealing its potential on surveillance and security application.
Object retrieval and localization with spatially-constrained similarity measure and k-NN re-ranking

One fundamental problem in object retrieval with the bag-of-visual words (BoW) model is its lack of spatial information. Although various approaches are proposed to incorporate spatial constraints into the BoW model, most of them are either too strict or too loose so that they are only effective in limited cases. We propose a new spatially-constrained similarity measure (SCSM) to handle object rotation, scaling, view point change and appearance deformation. The similarity measure can be efficiently calculated by a voting-based method using inverted files. Object retrieval and localization are then simultaneously achieved without post-processing. Furthermore, we introduce a novel and robust re-ranking method with the k-nearest neighbors of the query for automatically refining the initial search results. Extensive performance evaluations on six public datasets show that SCSM significantly outperforms other spatial models, while k-NN re-ranking outperforms most state-of-the-art approaches using query expansion.
3D model-based visual hand tracking

Capturing human hand motion through visual input is a challenging problem that involves the estimation of both global hand pose as well as the local finger articulation. This is a difficult task that requires a search in a high dimensional space due to the high degrees of freedom that fingers exhibit and the self occlusions caused by global hand motion. We propose a divide and conquer approach to estimate both global and local hand motion. The hand pose is determined from the palm using Iterative closed point (ICP) algorithm and factorization method. By incorporating natural hand motion constraints, we propose an efficient tracking algorithm based on a sequential Monte Carlo technique for tracking finger motion. Finally, the iteration step between the pose estimation and finger articulation tracking is performed in an EM fashion to obtain an accurate configuration estimation. Our experiments show that our approach is accurate and robust for natural hand movements.
Life cycle assessment tool for electromechanical products green design

Life cycle assessment (LCA) is a key technique to achieve green design. But it isn't applied widely because of its defects. In order to enhance the operability of LCA in green design, a LCA tool, named green product life cycle design (GPLCD) is described in this paper. The paper is divided into four sections: first, based on the analysis of the existing LCA method and the product design activities, the functional model of the LCA tool is built with IDEF0 methods. And then through analyzing the product and its life cycle activities, product's life cycle model is proposed and the functional unit class and process class are presented with object oriented design. Third, aimed to the problems of the data management of product's life cycle, product configuration management is integrated into GPLCD. Finally, a case on choosing material of impeller is given to testify how to use the LCA tool to support green design.
Robust nitrogen plasma immersion ion implantation treatment on gadolinium oxide resistive switching random access memory

In this paper, we demonstrate gadolinium oxide RRAM with nitrogen plasma immersion ion implantation (PIII) treatment technique at first time. For nitrogen plasma treatment, the nitrogen ions were incorporated with gadolinium oxide. We controlled the implantation voltage that the nitrogen ions exist near the surface of gadolinium oxide and it was forming a GdxOyNz layer. This can reduce the leakage current to reach a low current and power consumption operation. In addition, the retention and endurance characteristics were also improved.
Study of broadband near-field antenna for ultra-high-frequency radio frequency identification applications

A novel antenna for applications in an ultra-high-frequency (UHF) radio frequency identification (RFID) reader system is proposed and investigated. The antenna achieves a gain of lower than -10-dBi in all directions with strong magnetic field distribution over the entire UHF RFID band of 830-950-MHz as well as good performance in detecting tags when applied in UHF RFID systems. The antenna configuration is described and the principle of operation is explained. To investigate the effects of each parameter on the antenna performance, a comprehensive parametric study is carried out and the results are presented in detail and discussed. To prove the concept, a prototype is developed and fabricated. The measurement results demonstrate that the antenna can achieve a broad bandwidth and low gain, and produce a strong magnetic field with relatively concentrated field distribution in the near-field region of the antenna. Hence, this work is very promising for UHF near-field RFID reader applications.
Numerical simulation on the propagation of blast shockwave

Based on Any Lagrange-Euler algorithm( ALE), using ANSYS/LS-DYNA, the paper analyses the propagation of shockwave after encountering an elastic thin plate which is fixed around when an explosion takes place at altitude and on the rigid ground. The reflex rule is investigated when shockwave meets the plate with explosive in different distance to the plate in the air, as well as the reflection and diffraction rule when blast wave encounters the elastic paddle with the explosive on the ground respectively. The result shows that the reflex overpressure is about two times higher than the incident pressure when the shockwave meets the elastic thin plate; With the increase of distance between TNT and the thin plate, distance from initial point of Mach reflection to the projection of blasting center increases gradually; When the explosion occurs on the ground, reflection and diffraction happens when the shockwave encounters the wall, and Mach reflection will first takes place in 2H behind the wall. The research has certain reference value to the disaster protection of engineering structures.
A capacity comparison for SONET self-healing ring networks

The SONET technology employed in reducing the complexity, cost and number of transmission systems of a public network makes the creation of the telecommunication superhighway system for transporting signals for a wide range of services at high speed possible. But SONET technology can also create a certain degree of complexity in building a cost-efficient SONET network, especially in the case of SONET SHR (self-healing ring). It is a challenge to the network planner to find an efficient way to select the most economical SONET ring, or combination of rings, for demands between a set of nodes that are expected to be connected in a certain type of ring configuration. To gain the full benefit of the economics and services SONET might have to offer, effective ways for ring selection must be found. Routing decisions, especially for bidirectional rings, play a key role in deciding the ring capacities which the ring is supposed to meet. We present two efficient demand loading algorithms for BSHR, and analyze the results from their application on a variety of demand patterns, to find criterion for ring selection.<<ETX>>