nils-herrmann/clean_scirepeval huggingface dataset:
Study of Physical Self-Purification Capability in the Offshore Area of Jiaozhou Bay

Jiaozhou Bay is treasure of Qingdao which is given by nature. As the rapid development of industry and marine aquaculture, water quality has been severely polluted due to the discharge of large quantities of untreated industrial wastewater and urban sewage. Based on the Estuarine, Coastal and Ocean Model (ECOM), a three dimension Lagrangian particle model was built to simulate the trajectory of several pollution sources into Jiaozhou Bay and study the physical self-purification capability. The results presented here provided a scientific basis for the marine environmental protection and management. Jiaozhou Bay (JZB) is a typical semi-enclosed bay situated in the southern part of Shandong Peninsula (35o58´-36o18´N, 120o04´-120o23´E), with an area of 372 km 2 and an average depth of 7 m. In recent years, with the rapid development of industry and marine aquaculture, the pollution of JZB becomes more and more serious. One of the important reasons that cause the water quality deterioration can be attributed to the land source pollutant. The environment pollution problem increasingly aroused people's attention, thus, the research on the transportation of the land source pollutant and the physical self-purification capability has great significance in marine environmental protection and management. The 3-D Lagrange model can be given the 3-D trajectory, residence time, influence scope etc. and has widely applied in the ocean environmental protection realm (1) . The article set a particle tracking model base on the Estuarine, Coastal and Ocean Model (ECOM), simulate the range of the wastewater influence and study the physical self-purification capability of JZB, which provide a science basis for the local marine environmental protection. II. PARTICLE TRACKING MODEL A 3-D particle tracking model coupled with a hydrodynamic model was developed in this study. The hydrodynamic model was modified by introducing dry-wet grid technology (2-3) based on the Estuarine, Coastal and Ocean Model (ECOM), which provided the physical parameters (tidal elevation, current and eddy diffusion coefficients) to drive the particles transport process in the model. The variable boundary ECOM was successfully applied in Jiaozhou bay, Bohai bay and Jiaonan sea area (4-6) , therefore, the article briefly introduced the 3-D Lagrangian particle tracking. The trajectories of the particles were calculated following the scheme given by Dimou and Adam (7) . A random-walk particle tracing scheme has been designed which calculates the displacement of as the sum of an advective deterministic component and an independent, random-walk component which statistically approximates the turbulent eddy diffusion processes. The values of the diffusivities were taken from the hydrodynamic numerical model. Therefore, given the initial position of the particle as P(x,y,z,t), the position of the particles P(x,y,z,t+1) passed by a certain time step t were calculated as:
A Coordination Control Strategy of Voltage-Source-Converter-Based MTDC for Offshore Wind Farms

Similar to other major electrical apparatuses, the reliability and stability of the dc network is becoming the most important issue when using the voltage-source-converter-based multiterminal dc (VSC-MTDC) system for offshore wind power integration. A coordinated control strategy of VSC-MTDC named master–auxiliary is proposed by combining the advantages of the voltage margin and voltage droop control. This strategy has three advantages. First, the master converter station with the constant dc voltage control can provide reference to the system dc voltage and is helpful for the stabilization of dc voltage. Second, the integrated control of the dc voltage in both master and auxiliary converter stations are helpful for providing adequate active power control (APC) and restraining large power variation. Third, the APC converter station can serve as a backup for the dc voltage control in abnormal conditions. In order to guarantee the reliability and stability of the system under various operating conditions, this paper introduces the priority of dc voltage control to the coordination control strategy. Moreover, a parameter optimizing method of controllers for this strategy is also proposed. Finally, the effectiveness of the master–auxiliary control is verified by simulations under normal and abnormal conditions.
Registration of dynamic renal MR images using neurobiological model of saliency

In this paper we propose the use of a neurobiology-based saliency measure to improve the performance of a quantitative- qualitative measure of mutual information for rigid registration of 4D renal perfusion MR images. Our registration method assigns greater importance to more salient voxels by applying a soft thresholding function to normalized saliency values. The resulting saliency map is a better representation of what is truly visually salient than an entropy-based saliency map. Our tests on real patient datasets show that incorporating this saliency measure produces better registration results than traditional entropy-based approaches.
Optimization Design on Boiler-turbine Coordinated Control System with Automatic Generation Control

Automatic generation control (AGC) is an essential requirement of modern power system automatic control. At present, slow response to AGC load demand is a serious problem existing in thermal power units taking part in AGC, which makes the control performance of power system AGC poor. The unit one and unit two of Yaomeng Power Co. Ltd., are all once-through boiler, its boiler-turbine coordinated control system (CCS) is based on incremental state observer combined with conventional PID control, and the connection logic of CCS with AGC adopted the scheme shown in this paper. Through adopting the connection logic of CCS with AGC, the pre-add coal function can be realized in AGC mode, which assured the rapid response to AGC load demand of the power unit, and this viewpoint has been fully proved by the trend curves of AGC adjusting test of the two units.
Directional low-pass filtering for improved accuracy and reproducibility of stenosis quantification in coronary arteriograms

Considers the quantification of percent diameter stenosis in digital coronary arteriograms of low spatial resolution. To improve accuracy and reproducibility an edge-preserving smoothing method, called the directional low-pass filter (DLF), was developed to suppress quantum noise by averaging image intensity in a direction parallel to the vessel border. Accuracy of stenosis quantification was assessed by using stenosis phantoms. The standard error of the estimate (SEE) was 0.76 pixel-length (p) without spatial filtering and further reduced to 0.50 p by DLF; the average deviation as a measure of the regularity of border definition was also reduced by DLF from 1.00 to 0.68 p (n=50, P<0.001). It was shown that the DLF outperformed the conventional moving average filter and median filter. Reproducibility in terms of intraframe variability was assessed by using coronary arteriograms obtained from 10 patients. Intraframe variability of the percent stenosis measurements was reduced from 3.5% to 2.9% by DLF (n=10, P<0.005). An analysis of variance showed, however, that the interframe variability cannot be reduced by any of the spatial filters under investigation. The result of this study has provided a guideline for angiographically based quantification of percent stenosis under limited imaging resolution and suggests a new method for improving accuracy and reproducibility by directional low-pass filtering.
The effects of pH on the electrophysiological properties of the CNS of lymnaea stagnalis

The central nervous system is composed of many different interacting neurons all working together to provide sensory, motor, and visceral signals to the specific organism. The environment in which those neurons operate is crucial to the survival of the organism. Some environmental factors may include pH, temperature, or the intra-and extracellular ion concentration. If any of these factors are changed in a substantial way, the electrophysiological properties of the neuron may be affected. Some of the important electrophysiological properties of the neuron include the resting potential, the action potential, and the firing rate. These properties can be measured directly using sharp microelectrode methodologies. The performed experiments consist of recording the resting potential, action potential, and firing rate from a neuron in the CNS cerebral ganglion of the Lymnaea stagnalis. This paper addresses how various levels of pH affect the electrophysiological properties of neurons in the CNS of L. stagnalis.
Statistical Monitoring of Changes to Land Cover

Accurate detection of changes in land cover leads to better understanding of the dynamics of landscapes. This letter reports the development of a reliable approach to detecting changes in land cover based on remote sensing and radiometric data. This approach integrates the multivariate exponentially weighted moving average (MEWMA) chart with support vector machines (SVMs) for accurate and reliable detection of changes to land cover. Here, we utilize the MEWMA scheme to identify features corresponding to changed regions. Unfortunately, MEWMA schemes cannot discriminate between real changes and false changes. If a change is detected by the MEWMA algorithm, then we execute the SVM algorithm that is based on features corresponding to detected pixels to identify the type of change. We assess the effectiveness of this approach by using the remote-sensing change detection database and the SZTAKI AirChange benchmark data set. Our results show the capacity of our approach to detect changes to land cover.
The Study on Intelligent Advanced Fighter Air Combat Decision Support System

The purpose of this paper is to develop intelligent advanced fighter air combat decision support system (IAFACDSS). IAFACDSS will generate the best strategy for advanced fighter's pilot in an air combat environment. In the IAFACDSS, we use fuzzy inference system (FIS) as decision-making mechanism that takes into account of human thinking ability and pilot physical limitations. Before executing FIS process, every future posture corresponding to pilot's decision-making space of advanced fighter and an enemy's fighter future posture must be predicted. This paper assumes that IAFACDSS will accurately predict an enemy fighter's future posture. Then IAFACDSS applies the predicted data to generate the best control command through FIS. We verify the proposed algorithm with a set of fighter flying data that is used as enemy fighter's flying trajectory. The simulation results show that IAFACDSS will enable advanced fighter to obtain the best status in an air combat environment, thus effectively achieving the object for developing IAFACDSS
Sharing evolution genetic algorithm for global numerical optimization

In this paper, a sharing evolution genetic algorithms (SEGA) is proposed to solve global numerical optimization problems. In the SEGA, three strategies are proposed, which are population manager, sharing cross-over and sharing mutation, for effective increasing new born offspring's solution searching ability. Experiments were conducted on CEC-05 benchmark problems which included unimodal, multimodal, expanded, and hybrid composition functions. The results showed that the proposed method exhibits better performance when solving these benchmark problems compared to recent variants of the genetic algorithms.
Computationally Efficient Spectrum Sensing With Nyström Feature Template Matching

Kernel feature template matching (KFTM) was developed in 2014 for spectrum sensing in cognitive radio. However, the high computational burden involved in the kernel matrices construction and the related eigenvalues decomposition (EVD) limits its practical applications. This letter addresses this issue and proposes a Nyström feature template matching (NFTM) solution. Based on the randomly chosen subset of the whole data, the Nyström approximation is carried out to obtain the principle eigenvector of a given kernel matrix. With the approximated feature templates, we then modify the similarity (inner product) as a novel detection rule. Simulations using the real-world measurements of digital television (DTV) signal confirm that the proposed NFTM algorithm with about one-third samples provides almost the same performance as the KFTM method.
The study on intelligent vehicle collision-avoidance system with vision perception and fuzzy decision making

This paper proposes a combination scenario of vision perception and fuzzy decision making for developing an intelligent vehicle collision-avoidance system (IVCAS). In IVCAS, a CCD camera is installed on the following vehicle and used to capture the image of leading vehicles and road information. The features of the leading vehicles and lane boundary are recognized by vision perception method, which derived from our previous work on histogram-based color difference fuzzy c-means (HCDFCM). HCDFCM is a robust and fast algorithm for detecting object boundary. In this paper, we adopted the coordinate mapping relationship (CMR) with HCDFCM to provide a robust vision perception for the necessary information such as relative velocity, relative distance between leading and following vehicle and absolute velocity of following vehicle, etc. The collision-avoidance strategy is based on the vision perception and implemented by a fuzzy decision making mechanism. In this paper, the necessary information is integrated as a degree of exceeding safe-distance (DESD) to estimate the possibility of collision. A safety coefficient (SC) is defined to indicate the degree of safety. Therefore, the number of fuzzy rules that based on DESD and SC could be reduced to improve the efficiency of decision making. In addition to robust image processing, abundant information are derived from recognizing image feature using the proposed algorithm in this paper. The fuzzy decision making mechanism abstract useful compact data extracted from these abundant information. Therefore, the main advantage of IVCAS is using less number of fuzzy rules than other systems, and gets more effectiveness in vehicle collision-avoidance.
Design and Implementation of DSP-based License Plate Detection System

This paper focuses on the design of hardware framework about DSP-based embedded License Plate Detection system including the detailed technology of circuit design among image sensor, DSP, Flash, SDRAM, timer and resetting. The program on license plate detection has been tested on the designed board, it run all right with natural light condition, and the location of license plate is accurate. The size and cost of the board are small, but it is fast and highly reliable.
Electrically Modeled Reciprocal Inhibitory Oscillator

A model of the neuronal reciprocal inhibitory oscillator was developed in this study by use of an entirely electronic platform. Neuronal Oscillators were widely studied in previous research due to their important roll as central pattern generators. Our model consisted of two electronic components, the neuron emulator for modeling the electrical activities of a neuron and the artificial synapse for mimicking a synaptic connection between two neurons. The typical bursting patterns of action potentials were demonstrated by connecting two Neuron Emulators with the Artificial Synapse. These electronic devices provided a unique hardware platform for studying reciprocal inhibitory oscillators and other neuronal networks.
Relations of Interest and Interaction Between Rural Development Project Applicants and Approvers

Given established project applicants' conditions and established project requirements of development agencies, whether a development project will be approved depends on the approvers. And whether approvers approve the project in accordance with the established criteria is subject to: (1) whether the applicants pay kickbacks, (2) the development agencies' management systems, including the reward and punishment system, and (3) the degree of importance of the development project.
Adaboost-based algorithm for human action recognition

This paper presents a computer vision-based methodology for human action recognition. First, the shape based pose features are constructed based on area ratios to identify the human silhouette in images. The proposed features are invariance to translation and scaling. Once the human body features are extracted from videos, different human actions are learned individually on the training frames of each class. Then, we apply the Adaboost algorithm for the classification process. We assessed the proposed approach using the UR Fall Detection dataset. In this study six classes of activities are considered namely: walking, standing, bending, lying, squatting, and sitting. Results demonstrate the efficiency of the proposed methodology.
Time-Delay Estimation Using Quaternion

This paper addressed to research time-delay of two sensors in space. We established a quaternion model using the observed value of two sensors, and constructed Toeplitz matrix of that quaternion model. After analyzing the Toeplitz matrix, we presented our estimation algorithm. Finally, we used right-eigenvalue decomposition of quaternion matrix to construct corresponding noise subspace and then Quaternion-MUSIC method was used to estimate time-delay when the frequency of signal was known. Simulation illustrated that our method was effective.
The study of man-machine interface design based on Kansei engineering and with the center on users

This paper starts with the concept of Kansei engineering and man-machine interface to discuss the relationship between them. The paper also further analyzes visual perception elements of man-machine interface which is based on kansei engineering, i.e. color and image, and their application in man-machine interface design. After in-depth analysis, it's concluded that kansei engineering with the center on users plays a significant part in man-machine interface design.
Statistical control chart and neural network classification for improving human fall detection

This paper proposes a statistical approach to detect and classify human falls based on both visual data from camera and accelerometric data captured by accelerometer. Specifically, we first use a Shewhart control chart to detect the presence of potential falls by using accelerometric data. Unfortunately, this chart cannot distinguish real falls from fall-like actions, such as lying down. To bypass this difficulty, a neural network classifier is then applied only on the detected cases through visual data. To assess the performance of the proposed method, experiments are conducted on the publicly available fall detection databases: the University of Rzeszow's fall detection (URFD) dataset. Results demonstrate that the detection phase play a key role in reducing the number of sequences used as input into the neural network classifier for classification, significantly reducing computational burden and achieving better accuracy.
Optimal disturbance rejection control with application to offshore platforms

The problem of optimal disturbance rejection control (ODRC) with zero steady-state error to sinusoidal disturbances for stochastic discrete-time systems is considered. Based on the internal model principle, external sinusoidal disturbance is compensated by constructing a disturbance compensator. Then, an optimal linear quadratic Gaussian (LQG) controller with an infinite-time horizon cost functional is designed. The state estimator from measurements of the output makes the dynamic ODRC law physical reliable and the convergence of the cost functional can be guaranteed. Then the obtained ODRC law is applied to offshore platforms subjected to sinusoidal disturbance mainly from the ocean wave forces. The offshore structure is simplified into a single degree of freedom (SDOF) system. Simulation results show the presented control scheme is efficient in reducing the displacement and velocity of the offshore structure.
Study on Control Strategies to Improve the Stability of Multi-Infeed HVDC Systems Applying VSC-HVDC

Against the stability of the multi-infeed HVDC (MIDC) transmission system, voltage source converter based HVDC (VSC-HVDC) is applied to MIDC systems. The new systems can reduce probability of commutation failure and improve the stability of the MIDC systems. A physical model of a multi-infeed HVDC and VSC-HVDC system is developed, and its mathematical model is analyzed. The rectangular coordinate form of VSC's power transmission equations is deduced through coordinate conversion. Based on the inverse system method for multivariable nonlinear control, the nonlinear controllers of the multi-infeed HVDC and VSC-HVDC system are designed. Simulation results by PSCAD/EMTDC show that the designed nonlinear controllers improve effectively the voltage stability of the multi-infeed HVDC and VSC-HVDC system under small or large disturbances, such as short circuit fault, reduce the probability of commutation failure in the inverter of HVDC systems and increase the operation reliability of HVDC systems
Intelligent data cluster algorithm based on hierarchical FCM and Mahalanobis distance

Cluster method is used to categorize the same type of data points together. Fuzzy C-Means Clustering (FCM) has better cluster performance than traditional hard c-means clustering method. In the algorithm of FCM, the initial membership matrix of data is assumed in random normally, and the initial value affects the performance a lot meanwhile. In our previous study, a proposed hierarchical based FCM algorithm can give the proper initial value. In general, FCM based on Euclidean distance evaluate the membership value matrix to separate the data points into several clusters, but there are some data points can not be separated properly. Hence, this paper employs the Mahalanobis distance to improve the drawback of Euclidean distance function based method. The experiment results show that the proposed method can separate the data points well.
The DAGC algorithm design for large dynamic range & high sensitivity radar digital receiver

Automatic gain control (AGC) is an important way to expand input dynamic range and improve sensitivity of radar receiver. Digital automatic gain control (DAGC) is widely used in large dynamic range & high sensitivity radar digital receiver. In this paper, we propose a DAGC algorithm for radar digital receiver, and analyze the property of algorithm. Next, a simplified implementation of the algorithm is developed and simulated. Simulation results show that the designed DAGC algorithm can rapidly adjust gain to satisfy the needs of gain control in large dynamic range & high sensitivity radar digital receiver.
False data injection attacks with local topology information against linear state estimation

False data injection attacks (FDIAs) have been introduced as a critical class of cyber attacks against smart grid's monitoring system. These attacks aim to compromise the reading of grid sensors and phasor measurement units. It was shown that FDIAs can pass the traditional bad data detection. Furthermore, to perform an FDIA, the attacker need to acknowledge the power grid topology and transmission-line admittance value, which is not easy to obtain. In this paper, we propose a novel false data injection attack approach, called false data proportional attacks (FDPAs), which could avoid the traditional bad data detection method and do not need the transmission-line admittance value, but only local grid topology. When the measurement of a bus and transmission-line data is changed, simultaneously inject the same false data proportionally for all the buses and transmission-line that are connected to it. We demonstrate the success of these attacks with simulation by IEEE 30-bus test systems.