nils-herrmann/clean_scirepeval huggingface dataset:
Adaptive vibration control of a bridge and heavy truck

Bridge vibration control is an important issue whose purpose is to extend the structural service life of steel bridges. Normally, the bridge is modeled as an elastic beam or plate subject to a moving vehicle. However, the moving truck on a bridge is a complicated problem that must still be researched. In this paper, we propose a solution using a time varying model coupled with a black box identification method. This solution does not require an exact physical model. It uses a hybrid multi-channel adaptive vibration control technique that combines feed forward and feedback active control. Our system is called the Intelligent Vehicle/Bridge System (IVBS).
Design of analog MRAS controllers using operational amplifiers for motion control systems

This paper proposes a new procedure for designing a direct analog adaptive controller that is based on Model Reference Adaptive Systems (MRAS) using Operational amplifiers (Op-amps). The adaptive controller in s-domain (complex frequency domain) is firstly designed using Lyapunov theory. It is quite simple, robust and converges quickly. The controller is then simulated and adjusted using Matlab/Simulink software. Next, the plant and the controller in s-domain will be converted to equivalent corresponding continuous electronic circuits and continued to simulate using Multisim software before implementing to the real setup. With the same input reference the simulation results of the adaptive controlled system in s-domain, also in equivalent analog electronic circuits, and experimental results in the real setup are almost the same.
A fast computing algorithm for microgrid fault protection system using communication-assisted digital relays and initially experimental results

This paper presents a fast computing algorithm (FCA) for microgrid (μgrid) fault protection system that can online work with high adaptability and dependability functions and can get a critical fault clearing time shorter than two cycles beginning at fault inception points. Concretely, the FCA uses an overcurrent protection principle to protect branch lines that contain either loads or distributed energy sources in a μgrid system. In addition, in order to protect main lines in the μgrid from fault occurrences, a new fault protection method is developed through using communication-assisted digital relays. The main lines are understood as lines used to interconnect two or more distributed energy sources and not including any load branches along these lines. Two fundamental ways that are used to inspect operation of the H-grid protection system with the FCA are: (i) theoretical calculations from actual results of staged fault tests, motor-starting tests, and tests of μgrid's operation transition between grid-connected and islanded modes which are conducted at a real low-voltage AC μgrid in Taiwan and (ii) doing different fault tests along with FPGA boards to evaluate the FCA from a lab environment. As a result, the FCA can quickly detect and identify different types of grounding fault and optimally isolate faulted sections. Fault clearing time of the FCA for occurrences at the main lines is about 0.5-2 cycles depending on the algorithm's computation time and the communication time among the digital relays, while that at the branch lines is completely dependent on their fault current values.
All-optical two-way relaying dual-hop FSO systems using network coding over atmospheric turbulence channel

All-optical two-way relaying is an advanced techniques that helps to improve the data-rate and throughput of free-space optical (FSO) communication systems. In this paper, we propose to implement all-optical two-way relaying in dual-hop FSO systems using network coding. The relay node that is based on optical regenerate-and-forward relaying is equipped with an all-optical XOR gate and optical hard-limiters. Performance of the proposed system is analyzed under the effect of atmospheric turbulence and noise induced by the background light. Based on the closed-form expression for bit-error-rate (BER) derived in this paper, the numerical results demonstrate the feasibility of the proposed systems. In addition, the numerical results provide the useful information for system design such as optimum range of threshold power, required transmitted power, and supportable transmission distance.
Design of Reduced-Order Positive Linear Functional Observers for Positive Time-Delay Systems

This brief considers a new problem of designing reduced-order positive linear functional observers for positive time-delay systems. The order of the designed functional observers is equal to the dimension of the functional state vector to be estimated. The designed functional observers are always nonnegative at any time and they converge asymptotically to the true functional state vector. Moreover, conditions for the existence of such positive linear functional observers are formulated in terms of linear programming. Numerical examples and simulation results are given to illustrate the effectiveness of the proposed design method.
A Global Optimization Algorithm for Electromagnetic Devices by Combining Adaptive Taylor Kriging and Particle Swarm Optimization

This paper presents an efficient optimization strategy which employs adaptive Taylor Kriging and Particle Swarm Optimization (PSO). In this method, the objective function of electromagnetic problem is interpolated by using adaptive Taylor Kriging, in which the covariance parameter is obtained by Maximum Likelihood Estimation (MLE). And then, PSO is used to search for optimal solutions of electromagnetic problem. The proposed algorithm is verified its validity by analytic functions and TEAM (Testing of Electromagnetic Analysis Method) problem 22.
An open source Verilog front-end for digital design analysis at word level

We develop an open source Verilog front-end that compiles a digital circuit design into the circuit description at high level. Such description is a component net-list at a higher level than the gate net-list. In the component net-list, all high level information such as bit vector data-paths, finite state machines and counters are retained. Thus, the proposed front-end is suitable for newly proposed back-end algorithms that use high level information to synthesize, optimize and verify the circuit. Our front-end is able to parse large open-source designs.
Application of multitemporal ERS synthetic aperture radar in delineating rice cropping systems in the Mekong river delta

The aim of this study is to use multitemporal ERS-2 satellite synthetic aperture radar (SAR) images in delineating and mapping areas under different rice cropping systems in the Mekong river delta, Vietnam. Five change index maps were generated from the six images acquired between June and December, 1996. Using a 3-dB threshold, the pixels in each change index map were classified into one of three classes: increasing, decreasing or constant backscattering. The five change index maps were used to generate a composite map with 243 possible change classes. After discarding the minority classes, and merging the dominant classes with similar backscatter time series, the thematic classes of rice cropping systems practices in the study area were delineated.
An alternative approach to avoid overfitting for surrogate models

Surrogate models are data-driven models used to accurately mimic the complex behavior of a system. They are often used to approximate computationally expensive simulation code in order to speed up the exploration of design spaces. A crucial step in the building of surrogate models is finding a good set of hyperparameters, which determine the behavior of the model. This is especially important when dealing with sparse data, as the models are in that case more prone to overfitting. Cross-validation is often used to optimize the hyperparameters of surrogate models, however it is computationally expensive and can still lead to overfitting or other erratic model behavior. This paper introduces a new auxiliary measure for the optimization of the hyperparameters of surrogate models which, when used in conjunction with a cheap accuracy measure, is fast and effective at avoiding unexplained model behavior.
A Gaussian Mixture Model for Mobile Location Prediction

Location prediction is essential for efficient location management in mobile networks. In this paper, we propose a novel method for predicting the current location of a mobile user and describe how the method can be used to facilitate paging process. Based on observation that most mobile users have mobility patterns that they follow in general, the proposed method discovers common mobility patterns from a collection of user moving logs. To do this, the method models cell-residence times as generated from a mixture of Gaussian distributions and use the expectation maximization (EM) algorithm to learn the model parameters. Mobility patterns, each is characterized by a common trajectory and a cell-residence time model, are then used for making predictions. Simulation studies show that the proposed method has better prediction performance when compared with two other prediction methods.
Control of power in an island microgrid using adaptive droop control

In this paper, proposed an adaptive droop controller using compensate for the mismatch in voltage drops across line impedance, to enhance reactive power sharing accuracy in island microgrid. Communication is used to facilitate the tuning of adaptive controller for the change of load. The method will ensure in accurate reactive power sharing even the communication is interrupted if the load does not change during this time. If the load changes while the communication is interrupted, the accurate sharing is reduced, but the proposed method is better than the conventional droop control method. In addition, the accuracy reactive power sharing base on the proposed method is not affected by the time delay in the communication channel. The feasibility and effectiveness of the proposed method are demonstrated by using simulation on Matlab/Simulink.
Deep learning based supervised hashing for efficient image retrieval

Due to its storage and search efficiency, hashing has attracted great attentions in large-scale vision problems such as image retrieval and recognition. This paper presents a novel Deep Learning based Supervised Hashing (DLSH) method by using a deep neural network to better capture the semantic structure of nonlinear and complex data. We consider learning a nonlinear embedding that simultaneously preserves semantic information and produces nearby binary codes for semantically similar data. Specifically, our hashing model is trained to maximize the similarity measure of neighbor pairs while preserving the relative similarity of non-neighbor pairs with a relaxed empirical penalty in the binary space. An effective regularizer for minimizing the quantization loss between the learned embedding and the binary codes is also considered in the optimization to generate better hash code quality. Experimental results have demonstrated the proposed method outperforms the state-of-the-art methods.
Designing dual-band antenna for WiMAX

This paper concentrates on studying, designing and manufacturing a dualband microstrip antenna, that is able to operate within 2.5 – 2.69 GHz (2.5 GHz band ) and 3.4 – 3.6 GHz (3.5 GHz band). Antenna is made from FR4-epoxy substrate having ε = 4.4, h= 1.6 mm (thickness). It has folder structure and includes three branches: two resonant branches and tuning branch which are fed by 50 Ω microstrip line.
Accelerating Range Query Execution of In-Memory Stores: A Performance Study

The emergence of advanced technologies such as InfiniBand and Non-Volatile Memory together with decreasing in DRAM prices has enabled us to build ultra-low latency in-memory stores for backing real-time and large-scale services. Many studies have taken those opportunities to accelerate lookup operations of key-value stores. Meanwhile, there is a lack of researches that utilize them for improving range query performance. This paper reports a detailed performance analysis of range query execution of in-memory stores. Our findings show that in modern architecture, copying/serialization data for transmission over network is the major overhead of range query processing. To solve this issue, several methods could be considered, such as parallelism, caching, and utilizing RDMA Read. We have conducted some experiments to evaluate their potentials and surprisingly, the outcomes revealed that none of those performed well in every scenario. Optimizing performance gain when employing those techniques requires us to carefully address user behaviors, characteristics of data and even system architecture.
A low sidelobe fan-beam series fed linear antenna array for IEEE 802.11ac outdoor applications

This paper proposes a double-sided printed dipole linear array antenna with high gain, low sidelobe level (SLL) for IEEE 802.11ac outdoor applications. In this work, to gain low SLL, a series fed network, which has the output powers being proportional to the Chebyshev weight distribution, has been proposed. The radiating elements used to construct the array are double-sided printed dipoles (DSPD) and each single element is equally distributed on the feed network. Furthermore, to increase the directivity of the antenna, Yagi antenna theory is applied by adding driven elements above every single element and a reflector is constructed at the back of the proposed structure. The simulated results show that the proposed array antenna can operate at 5 GHz with a bandwidth of 211 MHz (5.393 GHz–5.604 GHz). The gain and SLL acquired are 17.5 dBi and −26.0 dB at the frequency of 5.5 GHz, respectively.
A Novel Algorithm for Multi-class Cancer Diagnosis on MALDI-TOF Mass Spectra

Mass spectrometry (MS) has been used to generate protein profiles from human serum, and proteomic data obtained from MS have attracted great interest for the detection of cancer. Because MALDI-TOF MS provides high-resolution measurements, the biomarker identification has been limited by the unbalance problem between high-dimensional attributes and small sample-size. To deal with the multi-class problem in cancer prediction and biomarker identification, we propose a fast and robust multi-class cancer classification framework. A novel MS biomarker selection algorithm is provided by utilizing over sampled wavelet transform to extract wavelet coefficients and statistical testing to select features. The multi-class Gentle AdaBoost is used as a classifier due to its efficient classification procedure. Several experiments are deployed on real MALDI-TOF MS data in order to prove the superiority of proposed method compared to previous algorithms. The experimental results show that our proposed framework is an effective tool for analyzing MS data in cancer detection.
A Model of Electromagnetic Interferences Induced by Corona Discharges for Wireless Channels in Substation Environments

The design of robust receivers in environments with strong impulsive noise may be challenging due to their high-amplitude events and their rare but nonnegligible occurrence. In substation environments, these impulsive noises are predominant. They are induced by corona discharges such as electrical arcs or partial discharges in the air. The emitted radiations can occupy a wide range of frequencies typically reaching a few gigahertz with very high amplitude. In this paper, we develop a generalized model of impulsive electromagnetic interference induced by corona discharges for wireless channels. Radiations emitted by these sources are generally transient impulses, their occurrences follow cyclostationary process, due to their generation by an alternative current. In this model, we both take into account the physical mechanism of these discharges and the induced electromagnetic radiations. Experimental results validate the approach in terms of first- and second-order statistics such as amplitudes, interarrival time, occurrences, and power spectrum densities of impulsive transient noise.
An Extreme Algorithm for Network-Topology Construction Based on Constrained Delaunay Triangulation

This paper presents a fast algorithm for network topology construction. The algorithm works in two phases: input analyzing, and topology construction. In the first phase,inconsistencies in input are solved based on constrained Delaunay triangulation. The second phase consists of two parts:the sorting edges construction, and establish the topology relationship. The time of the first part highly depends on the input data distribution, and the second part, which presents our solution, runs in O(NlogN), what is confirmed by experiments using real data from geographic database.
An operational approach for analyzing ICT-based constructivist and adaptive learning systems

Constructivism is a learning theory that states that people learn best when they actively construct their own knowledge. Adaptability is the ability to adapt learning ex- periences to different kinds of learners. A significant number of ICT-based constructivist learning systems and ICT-based adaptive learning systems have been proposed in recent years. A critical problem related to the design and use of these kinds of systems has been the lack of a practical means for evaluat- ing the conformity of existing systems with constructivist prin- ciples and with adaptability principles. Our research aims to design truly constructivist and adap- tive learning environments. For that purpose, we have pro- posed a set of operational criteria for certain aspects of con- structivism and a set of criteria for adaptability from a con- structivist point of view, and we have used them as a useful framework both for designing learning systems and for ana- lyzing learning systems. In this paper, we present a new approach using operational criteria for analyzing existing learning systems with respect to support for constructivism and support for adaptability. We also outline several promising directions for future research on how to make existing learning systems more constructivist and adaptive.
Budgeted Phylogenetic Diversity on Circular Split Systems

In the last 15 years, phylogenetic diversity (PD) has gained interest in the community of conservation biologists as a surrogate measure for assessing biodiversity. We have recently proposed two approaches to select taxa for maximizing PD, namely PD with budget constraints and PD on split systems. In this paper, we will unify these two strategies and present a dynamic programming algorithm to solve the unified framework of selecting taxa with maximal PD under budget constraints on circular split systems. An improved algorithm will also be given if the underlying split system is a tree.
56-Gbps 4-PAM system over nearly 40 km transmission by employing an O-band EML and SOA

This work demonstrates a 56-Gbps 4-PAM EML-based transmission system. Using an O-band SOA to increase power budget and FFE/DFE to compensate SOA-induced ISI, 37.8-km transmission is successfully implemented with BER below the FEC limit (2 × 10−4)
Deep Recurrent Neural Networks for Winter Vegetation Quality Mapping via Multitemporal SAR Sentinel-1

Mapping winter vegetation quality is a challenging problem in remote sensing. This is due to cloud coverage in winter periods, leading to a more intensive use of radar rather than optical images. The aim of this letter is to provide a better understanding of the capabilities of Sentinel-1 radar images for winter vegetation quality mapping through the use of deep learning techniques. Analysis is carried out on a multitemporal Sentinel-1 data over an area around Charentes-Maritimes, France. This data set was processed in order to produce an intensity radar data stack from October 2016 to February 2017. Two deep recurrent neural network (RNN)-based classifiers were employed. Our work revealed that the results of the proposed RNN models clearly outperformed classical machine learning approaches (support vector machine and random forest).
Carbon nanotubes based lubricating oils for engines

Carbon nanotubes (CNTs) are well-known nanomaterials with many excellent properties such as high hardness, high strength, and excellent thermal conductivity. Owing to their very high thermal conductivity (2000 W/m.K compared to thermal conductivity of Ag 419 W/m.K), CNTs become ones of the most suitable nano additives for fabricating the lubricating oils in order to increase the thermal conductivity of lubricating oils, to enhance the efficiency of heat dissipation for the engine, and to improve the performance efficiency of engine. In this work, we present the obtained results on application of the CNTs in lubricating oils for some engines. The results showed that with the addition of CNTs, the thermal conductivity of lubricating oils increase about 15%, this helps improve the efficiency of heat dissipation for the engine. Experimental results show that when using the lubricating oils containing carbon nanotubes, the temperature of engine dropped about 10°C, fuel saving was upto 15% and longevity of lubricating oil increased upto 20,000 km.
A compact dual-layer Electromagnetic Bandgap (EBG) structure

Several of compact Electromagnetic Bandgap (EBG) structure was proposed in the literature. In these structures, the size reduction was achieved by increasing either equivalent capacitance or equivalent inductance. In this paper, a compact dual-layer EBG structure was proposed by producing of both additional capacitance and parasitic capacitance. Therefore, the proposed EBG structure exhibit the cell size reduction of about 62% in comparison with the conventional mushroom-like EBG.
A precoding method for closed-loop MIMO-OFDM systems

This paper presents a precoding method for closed loop MIMO-OFDM systems. For precoding, the codeword is selected by comparing the unitary channel matrix, obtained from the SVD method, with a predefined codebook. The performance of closed and open-loop MIMO-OFDM systems is investigated for the case of perfect and imperfect CSI, as well as for different level of quantization. The simulation results show that the first one outperforms the later one in all case. The system performance gain depends on the number of bits using for feedback channel.
A New Method for Boundary-Based Shape Matching and Retrieval

This paper presents a novel method for efficient boundary based shapes matching and retrieval in presence of occlusion. In this method, the geometric and topological information of boundary curves are encoded in the form of longest common subcurves (LCS) graphs and their similarity is estimated by graph matching. B-Spline is used for approximating the original boundary, then inflection points are detected to split such a B-spline to convex/concave segments. The characteristic string is constructed based on these segments' canonical frame. After LCS candidates are found, their graphs which are constructed by using its segments as vertices and the weighted walkthrough (WW) between two segments as edges are compared to obtain the optimal match. Thorough experimental results and comparisons demonstrate that our method outperforms traditional LCS or dynamic programming based methods in shape matching and enhances the quality of inexact shape retrieval, in particular in the presence of occlusions and affine transformation.