nils-herrmann/clean_scirepeval huggingface dataset:
Predicting Membrane Protein Types with Dimensionality Reduction and Kernel Method

The membrane protein type is an important feature in characterizing the overall topological folding type of a protein or its domains therein. How to fast and efficiently annotate the type of an uncharacterized membrane protein is a challenge. Some discrete models, such as DC (Dipeptide Composition) have been proposed to represent a protein sequence in the field of predicting membrane protein types. However, a high dimensional disaster may be caused by using this representation method. In this paper, a linear dimensionality reduction algorithm LDA (Linear Discriminant Analysis) and a nonlinear dimensionality reduction algorithm KLDA (Kernel Linear Discriminant Analysis) are introduced to solve this problem by extracting the indispensable features from the high-dimensional DC space, respectively. Based on the reduced low-dimensional features, K-NN (K-nearest neighbor) classifier is introduced to identify the types of membrane proteins. As a result, experiment results show that using the proposed method to cope with prediction of membrane proteins types is very effective. It also can be seen that the success rates obtained by LDA are higher than those by other dimensionality reduction method such as PCA.
Research of Speed Ratio Matching Strategies of Hydro-Mechanical Continuously Variable Transmission System for Tractor

To realize the optimal power and best fuel economy performance of engine, the speed ratio matching strategies of tractor with hydro-mechanical continuously variable transmission (HMCVT) system are studied. The engine output torque model and fuel consumption model are established based on engine test results. On the basis of the engine rotary speed feature, the target speed ratios of HMCVT system in the numerical table are given, which realizes the optimal power and best fuel economy performance mode within the speed ratio range of the HMCVT system. The tractive performance of the tractor equipped with HMCVT and original tractor are compared. The results show that the tractors assembled with HMCVT can gain a continuously variable speed. Additionally, in any traction force, the engine works near full load. As a result, the tractors with HMCVT can improve themselves productivity and fuel economy.
An Android/OSGi-Based Mobile Gateway for Body Sensor Network

The mobile remote monitoring system based on wireless body sensor network, as a new medical treatment, has been widely used in the modern medical field. Portable mobile gateway is the core of remote mobile monitoring. In this paper, we present an Android/OSGi mobile body sensor network gateway technology to dynamically load data transfer interface, automatically deploy graphical user interface and manage wireless sensors from remote server, which greatly reduce the workload of developers and complexity of personal operation. For the performance test, the Android/OSGi-based mobile body sensor network gateway reduces the consumption of Android phone memory and network flow, showing a good performance in remote management of wireless sensors.
Landmine-enhanced imaging based on migratory scattering in ultra-wideband synthetic aperture radar

Ultra-wideband Synthetic Aperture Radar (UWB SAR) imaging presents numerous challenges, but also opportunities to extract object-level information. The phenomenon of migratory scattering, which describes the aspect dependent spatial location of scattering center, is a source of information that can be mined for details about shape and size of objects. In this paper, we firstly analyze the migratory scattering phenomenon of landmine, and then a method of migratory scattering extraction is proposed. Further, we directly form the migratory scattering image instead of the conventional SAR image. Compared with the conventional SAR image, the migratory scattering image can show the information of type and size better. In the migratory scattering image, the landmine exhibits part of a circle. We can enhance landmine by circular Hough transform the migratory scattering image the migratory scattering image easily. The electromagnetic simulation result demonstrates the effectiveness of the proposed method.
Robust object tracking using Bi-model

Occlusion is one of the major problems that object tracking faces in a clustered environment. In this paper, a tracking method which can deal with partial occlusion is proposed. There are two novelties in this paper: (1) using SURF keypoints to represent the object, key-points are evaluated and online learned by Random Ferns. (2) Bi-model is proposed to store key-points from object and surrounding background. In each frame, key-points inside or around the object bounding box will be assigned labels by matching with points stored in the Bi-model. These labeled points will be further used for improving the tracking accuracy and learning of Random Ferns. Long-term tracking is achieved by combining detection and tracking together. Experiments on videos with occlusion conditions show that the proposed method has good performance on tracking partial occluded objects, compared to some of the state-of-art methods.
Dynamic Scheduling of Operating Reserves in Co-Optimized Electricity Markets With Wind Power

We propose a probabilistic methodology to estimate a demand curve for operating reserves, where the curve represents the amount that a system operator is willing to pay for these services. The demand curve is quantified by the cost of unserved energy and the expected loss of load, accounting for uncertainty from generator contingencies, load forecasting errors, and wind power forecasting errors. The methodology addresses two key challenges in electricity market design: integrating wind power more efficiently and improving scarcity pricing. In a case study, we apply the proposed operating reserve strategies in a two-settlement electricity market with centralized unit commitment and economic dispatch and co-optimization of energy and reserves. We compare the proposed probabilistic approach to traditional operating reserve rules. We use the Illinois power system to illustrate the efficiency of the proposed reserve market modeling approach when it is combined with probabilistic wind power forecasting.
Prediction on future DC power system

Analyzed some serious issues, including power energy quality and large area power interruption, require complex method to overcome in 3-phase power system, so, suggested to go other way, proposed independent full DC power system to become one of main power systems in the future. The authors emphasized the necessity and capability of full DC power systems in the significance of technology and energy saving, so that safer, more reliable, stable, flexible and controllable DC power grids could serve the people better than that of the present AC power systems. Presented brief structure of full DC power system where DC power would be produced from multi-phase generator-rectifier group in power station, consumers take power from DC bus, which benefits motor control and energy flow between loads connected the same DC bus without 3 phase balance problems, and DC-DC converters based Duty-Cycle control implement commutations at two terminals of HVDC transmission lines, which reduce the technical difficulties than that in 3-phase AC commutation station. Non-crystal material with about 15 kHz of frequency could be selected for advanced isolators, so as to get smallest power consumption. Some new technology lifting DC voltage in geometrical progression was suggested to be available at the sent terminal of HVDC line. With the development of renewable power systems and their energy storages, DC interconnection grids could be a better choice than AC interconnection grids because no frequency and phase shift problems need to be considered. Bulky 3-phase transformer with industrial frequency and vary kinds of compensators for AC power qualities could be saved. The authors suggested scientific policy for economically implementing full DC power systems step by step. It could be predicted that people would see 3 kinds of power systems, full AC, full DC and hybrid AC-DC systems operating simultaneously by the middle of 21 century.
On-Demand and Reliable vSD-EON Provisioning with Correlated Data and Control Plane Embedding

Software-defined elastic optical networks (SD-EONs) provide operators more flexibility to customize their optical infrastructure dynamically and adaptively, and network virtualization, i.e., infrastructure-as-a-service (IaaS), enables multiple tenants to share the substrate infrastructure efficiently. In this paper, we study how to provision virtual SD-EONs (vSD-EONs) with the correlated data and control plane embedding (&#967;-VNE) that considers the quality-of-service (QoS) of virtual control plane (vCP), i.e., availability and control channel latency. We propose a &#967;-VNE algorithm to solve the problem, design the network system to realize it, and accomplish proof-of-concept experimental demonstrations in an OpenFlow-based network testbed. Numerical and experimental results indicate that the proposed algorithm and system function well and can realize on-demand and reliable vSD-EON provisioning.
Synthetic Aperture Radar Imaging Using Stepped Frequency Waveform

This paper presents a synthetic aperture radar (SAR) imaging system using a stepped frequency waveform. The main problem in stepped frequency SAR is the range difference in one sequence of pulses. This paper analyzes the influence of range difference in one sequence of pulses in detail and proposes a method to compensate this influence in the Doppler domain. A Stolt interpolation for the stepped frequency signal is used to focus the data accurately. The parameters for the stepped frequency SAR are analyzed, and some criteria are proposed for system design. Finally, simulation and experimental results are presented to demonstrate the validity of the proposed method. The experimental data are collected by using a stepped frequency radar mounted on a rail.
TEFE: A Time-Efficient Approach to Feature Extraction

With the rapid evolution of Internet applications, people all over the world are sharing pictures, videos and audios online, and thus, content-based analysis is often demanded. Test efficiency is crucial to the success of online information processing. One obstacle to high-speed testing is the time cost of feature extraction for test objects, particularly for objects with complex representation such as images, videos and audios. In this paper, we study the problem of reducing test time cost by extracting cheap but sufficient features. We propose the TEFE (time-efficient feature extraction) approach, which balances between the test accuracy and test time cost by extracting a proper subset of features for each test object. In the implementation, TEFE trains a sequence of support vector machines and classifies each test object cascadingly. Empirical study shows that TEFE is time efficient while holding a classification accuracy close to that of using all features. It also shows that the test time is linearly adjustable in TEFE.
Duopoly electricity markets with accurate and inaccurate market goals

Electricity markets are complex systems due to their deregulation and restructuring. We develop an agent-based simulation model for a stylized electricity pool market and simulate the market as a repeated game. An online hill climbing with adjustment algorithm is applied to generator agents to guide them to bid strategically to reach their expected market share. It is observed that accurate (or genial) expected market goals lead to collusive behavior of generator agents with an equilibrium where their total profit is maximized. On the other hand, it is also found that inaccurate (or malicious) market goals could result in price war with an equilibrium where their profits are minimized.
An Isophase-Based Life Signal Extraction in Through-the-Wall Radar

In this letter, we propose an isophase-based method for human life signal extraction using a stepped-frequency continuous-wave through-the-wall radar. Isophase is the line connected by points of the same phase value in the slow time range map and is a key feature to distinguish a stationary human from mechanical vibrating targets. First, the isophase is introduced and associated linearly with human life signal, and the linear coefficient is greater than 1. Then the limitation of the isophase is discussed. Finally, the isophase is extracted by the edge detection method. The isophase expands the micromotion range and therefore robust to noise. Due to the sudden change from <inline-formula> <tex-math notation="LaTeX">$\pi $ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$- \pi $ </tex-math></inline-formula>, the isophase can remove outliers effectively. The simulation results verify its advantages over the amplitude peak locus. The experiments done with a stationary human and a rotating fan behind a wall show that the proposed method can extract human life signal effectively and precisely.
When Does Cotraining Work in Real Data?

Cotraining, a paradigm of semisupervised learning, is promised to alleviate effectively the shortage of labeled examples in supervised learning. The standard two-view cotraining requires the data set to be described by two views of features, and previous studies have shown that cotraining works well if the two views satisfy the sufficiency and independence assumptions. In practice, however, these two assumptions are often not known or ensured (even when the two views are given). More commonly, most supervised data sets are described by one set of attributes (one view). Thus, they need be split into two views in order to apply the standard two-view cotraining. In this paper, we first propose a novel approach to empirically verify the two assumptions of cotraining given two views. Then, we design several methods to split single view data sets into two views, in order to make cotraining work reliably well. Our empirical results show that, given a whole or a large labeled training set, our view verification and splitting methods are quite effective. Unfortunately, cotraining is called for precisely when the labeled training set is small. However, given small labeled training sets, we show that the two cotraining assumptions are difficult to verify, and view splitting is unreliable. Our conclusions for cotraining's effectiveness are mixed. If two views are given, and known to satisfy the two assumptions, cotraining works well. Otherwise, based on small labeled training sets, verifying the assumptions or splitting single view into two views are unreliable; thus, it is uncertain whether the standard cotraining would work or not.
Agent-based electricity market simulation with demand response from commercial buildings

With the development of power system deregulation and smart metering technologies, price-based demand response (DR) becomes an alternative solution to improving power system reliability and efficiency by adjusting the load profile. In this paper, we simulate an electricity market with DR from different types of commercial buildings by using agent-based modeling and simulation (ABMS) techniques. We focus on the consumption behavior of commercial buildings with different levels of DR penetration in different market structures. The results indicate that there is a noticeable impact from commercial buildings with price-responsive demand on the electricity market, and this impact differs with different scales of DR participation under different levels of market competitions.
On the usefulness of infeasible solutions in evolutionary search: A theoretical study

Evolutionary algorithms (EAs) have been widely used in optimization, where infeasible solutions are often encountered. Some EAs regard infeasible solutions as useless individuals while some utilize infeasible solutions based on heuristic ideas. It is not clear yet that whether infeasible solutions are helpful or not in the evolutionary search. This paper theoretically analyzes that under what conditions in- feasible solutions are beneficial. A sufficient condition and a necessary condition are derived and discussed. Then, the paper theoretically shows that the use of infeasible solutions could change the hardness of the task. For example, an EA-hard problem can be transformed to EA-easy by exploiting infeasible solutions. While, the conditions derived in the paper can be used to judge whether to use infeasible solutions or not.
When smart grid meets geo-distributed cloud: An auction approach to datacenter demand response

Datacenter demand response is envisioned as a promising tool for mitigating operational stability issues faced by smart grids. It enables significant potentials in peak load reduction and facilitates the incorporation of distributed generation. Monetary refund from the smart grid can also alleviate the cloud's burden in escalating electricity cost. However, the current demand response paradigm is inefficient towards incentivizing a cloud that runs over geo-distributed datacenters. Leveraging auction theory, this work presents an efficient incentive mechanism to elicit demand response from geo-distributed clouds. To determine the winning bids and their corresponding payments, the cloud that acts as the auctioneer needs to solve a set of winner determination problems that are highly challenging. By integrating techniques from the Gibbs sampling method and the alternating direction method of multipliers, we propose a decentralized algorithm for each datacenter to make autonomous decisions on winning bid selection and workload management, striking a balance among the economic efficiency, truthfulness and the computational efficiency. Through extensive trace-driven evaluations, we demonstrate that our incentive mechanism constitutes a win-win mechanism for both the geo-distributed cloud and the smart grid.
Occurrence pattern of Spodoptera litura in Hunan and its prediction methods

The occurrence regulation of Spodoptera litura was studied according to the continiuous years's data investigated in tobacco fields and weather data of Chenzhou and Ningyuan in Hunan. Results showed that generations of Spodoptera litura are 4–4.5 with the effective accumulated temperture per year and the actual generations of Spodoptera. litura is in accordance with the predicted generations in Hunan Province. The sources of Spodoptera litura in early stage of the year were migration pest, there were four occurrence stages in a year according the properties of pests sources, the number of trapped moths from April to June is significantly correlated with the number in March, the forecasting model is ŷ = 233.54 + 112.44x<inf>4</inf> (r = 0.815<sup>∗</sup>, n = 8). The number of trapped moths from April to June is significantly correlated with the daily average temperture of January (reliability degree was 90%), the forecasting model is ŷ = −1410 + 350x(r = 0.595, n = 9). The number of trapped moths of Agrotis ypsilon at its major migration stage (February-April), which synchronously migrated with Spodoptera litura, is significantly correlated with the number of Spodoptera litura in its major migration stage (April-June), and it provide data for the prediction of occurrence trend of Spodoptera. litura. The damage percent is significantly correlated with the number of Spodoptera litura per 100 plants, and the number of Spodoptera. litura per 100 plants at normal control plots as x<inf>1</inf>, the damage percent as y, r = 0.977<sup>∗∗</sup> (n=10); the number of Spodoptera litura per 100 plants at observed plots as x<inf>2</inf>, the damage percent as y, r = 0.974<sup>∗∗</sup> (n=10).
Image registration for P-band UWB SAR interferometry

Synthetic aperture radar (SAR) image registration is of fundamental importance for SAR interferometry (InSAR). In this paper, we investigate and propose an algorithm for P-band ultra-wideband (UWB) InSAR. By using a common ground reference plane when focusing the SAR images by the back-projection (BP) algorithm, a geometrical registration is performed simultaneously. Then the residual shifts corresponding to the topography are estimated by cross-correlation and interpolation operations. The accuracy of registration is evaluated by calculating the errors from the misregistration phases. Results with a P-band UWB InSAR system are shown to prove the validity of the proposed method.
Active Query Driven by Uncertainty and Diversity for Incremental Multi-label Learning

In multi-label learning, it is rather expensive to label instances since they are simultaneously associated with multiple labels. Therefore, active learning, which reduces the labeling cost by actively querying the labels of the most valuable data, becomes particularly important for multi-label learning. A strong multi-label active learning algorithm usually consists of two crucial elements: a reasonable criterion to evaluate the gain of queried label, and an effective classification model, based on whose prediction the criterion can be accurately computed. In this paper, we first introduce an effective multi-label classification model by combining label ranking with threshold learning, which is incrementally trained to avoid retraining from scratch after every query. Based on this model, we then propose to exploit both uncertainty and diversity in the instance space as well as the label space, and actively query the instance-label pairs which can improve the classification model most. Experimental results demonstrate the superiority of the proposed approach to state-of-the-art methods.
Performance of differential space-frequency modulation under time-varying frequency-selective fading channels

In this paper two differential subcarrier grouping schemes named MSSG and SSSG are firstly analyzed under the same mathematic model. Then we describe two differential space-frequency modulation schemes combining differential space-time modulation and orthogonal frequency division multiplexing. They are F-DSFM in which differential encoding is carried out in frequency direction and T-DSFM in which differential encoding is carried out in time direction. Analysis and simulation results show that F-DSFM is suit for strong time- selective fading channels and T-DSFM is suit for strong frequency-selective fading channels, under time-varying frequency-selective channels subcarrier grouping scheme SSSG is better than MSSG especially for low data rate transmission.
Visual vehicles detection and robustness enhancement

This article is about vehicles detection from visual data which is an important part of automotive driving assistance systems. It is a big challenge to make the vehicles detection more robust. In order to enhance the robustness, a lane lines stabilization methods is proposed by studying the imaging model. Next, a hypothesis generation and verification framework is applied for saving time. Finally, our approach is compared to the Entropy-verified method and DPM in runtime performance and false positive ratio. It turns out that our approach gets a balance between runtime performance and false positive ratio.