nils-herrmann/clean_scirepeval huggingface dataset:
Particle swarm optimizer for multi-objective problems based on proportional distribution and cross-over operation

In multi-objective particle swarm optimization (MOPSO) methods, selecting the best local guide (the global best particle) for each particle of the population provides great benefits on the convergence and diversity of solutions, especially when problems are optimized with a large number of objectives. This paper introduces the proportional distribution based particle swarm optimizer (PSO) with cross-over operation in external repository for dealing with multi-objective problems. It combines advantages of wide-ranged exploration with cross-over operation, to maintain diversity of new found non-dominated solutions via proportional distribution in the external repository, and deep exploitations of PSO to enhance the solution searching abilities of particles. Introduction of cluster and disturbance allows the proposed method to sift through representative non-dominated solutions from the external repository and prevent solutions from falling into the local optimal. Experiments were conducted on four common MO benchmark problems. The results showed that the proposed method accomplishes better in four performance metrics when solving these benchmark problems compared to three other related works.
Taguchi-based disturbance with tournament selection to improve on MOPSO

In this paper, a Taguchi-based disturbance mechanism is deployed in our preliminary study on MOPSO. The proposed scenario includes tournament selection for global best solutions, jump-improved operation to expand the searching space, cluster operation to improve the diversity, and Taguchi-based disturbance can enhance the searching ability and reduce the possibility of falling into local optima of particles. Experiments are conducted on seven two-objective benchmarks. The results show that the proposed method operates better than other algorithms in three performance metrics.
Parameter analysis of hybrid intelligent model for the prediction of rare earth stock futures

Because of rare earth futures stock variability and uncertainty of the market, many investors hope to be able to predict the price of rare earth futures on the stock market in the future. The neural network does do better than others in short-term forecasting, and there is no need to establish a complex nonlinear mathematical model and relationship. Based on these advantages, this paper uses the neural network based on genetic algorithm to predict the closing price of rare earth stock by analyzing the historical data of rare earth stock. In the genetic algorithm, the parameters such as crossover rate, mutation rate, iterations and population size are analyzed. Based on the parameter analysis results, a hybrid machine learning model which is suitable for the prediction of rare earth stock is established, which provides a reference for the investors.
An Examination of Underfill Flow in Large Dies With Nonuniform Bump Patterns

In this paper, numerical modeling and experimental results are presented for underfill flow in a large die with a nonuniform bump pattern in a flip-chip packaging configuration. Two different 2-D flow models coupled with the volume-of-fluid method are applied to track the underfill flow front during the simulation of the flip-chip encapsulation process. The first model employs the modified Washburn model and uses a time-dependent inlet velocity to account for the flow resistance across the gap direction in the presence of bump interconnects. The second model introduces a momentum source term in the Stokes equation to represent the gapwise flow resistance. Rheological properties, surface tension, and dynamic contact angles for commercial underfill material and the effect of flux residue on underfill wetting properties are experimentally determined. Simulation results based on the two models are compared with in-situ flow visualization conducted using bumped quartz dies. The modified Stokes model yields better predictions of the underfill penetration length as a function of time and the total flow-out time. This model is then used to investigate the effects of dynamic contact angles and temperature-dependent underfill viscosity on underfill flow in a large die with a nonuniform bump pattern.
Negative impacts of ecotourism on biological environment and protective countermeasures

This article makes new interpretation of impacts of ecotourism from the perspective of biological environment, and points out negative impacts of ecotourism on biological environment, which are water attractions, soil and vegetation, as well as wildlife. On this basis, a detailed analysis of the negative impacts is made to reveal deep-rooted causes, and finally the tentative protective countermeasures to solve the problems are put forward: cultivation of environmental protection awareness, implementation of relevant legislation and regulation as well as limiting numbers within the carrying capacity of biological environment.
Research on the power combining efficiency of a shipborne X-band transmitter

A power combining solution using magic-T is presented to apply in a shipborne X-band transmitter. According to scatter parameter theory, the factors that have influence on combining efficiency are analyzed; the combining efficiency and max output power are calculated. The results show that: (1) the magic-T has the advantage of small size, simple structure, good heat dissipation performance, so that it suits the shipborne situation well. (2) The combining efficiency of magic-T depends mostly on the insertion loss, and the amplitude and phase of input signal. (3) The 1kW output can be satisfied using the power combining solution presented.
On detecting and adaptive timing for electromyogram based control signals

The purpose of this study was to develop and evaluate a signal-processing algorithm for controlling electromechanical devices by use of electromyogram (EMG) signals acquired with surface electrodes. The goal was to provide a robust trigger pulse with an appropriate duty cycle that, to some extent, reflects the duration of the muscle contraction. The EMG signal was amplified, digitized, and processed by an 8-bit microprocessor. The Multiplication of Backward Differences (MOBD) algorithm, originally designed to detect the QRS complexes in the electrocardiogram, was used to detect the on-set of the EMG signal. An adaptive refractory period scheme was devised to adjust the trigger pulse width based on EMG activities after the on-set point. This paper describes the development of the microprocessor based system and the preliminary test results.
Compressed sensing MIMO radar waveform optimization without signal recovery

It has been proved that compressed sensing (CS) is an efficient technique in MIMO radar system due to their ability to significantly reduce the computational burden. In this paper, we present an iterative waveform optimization algorithm for compressed sensing MIMO radar without signal reconstruction. Using prior information of target and clutter, the transmitted waveforms and the receiving filters are optimized alternatively. By maximizing SINR, the high detection performance of compressed sensing MIMO radar is guaranteed. Without reconstructing the signal, the complexity in receiver can also be decreased. We calculate the SINR of MIMO radar waveforms and compare with its own upper bounds and the SINR corresponding to the LFM waveforms. The numerical results show that using the algorithm in waveform design process, the SINR of compressed sensing MIMO radar waveforms is improved significantly compare to the LFM waveforms.
Cluster-based Adaptive Mutation Mechanism To Improve the Performance of Genetic Algorithm

This paper discusses the improvement of premature convergence in genetic algorithm (GA) used for optimizing multimodal numerical problems. Mutation is the principle operation in GA for enhancing the degree of population diversity, but is not efficient often, particularly in traditional GA. Moreover, the definition of mutation rate is a tradeoff between computing time and accuracy. In our work, we introduce the cluster method nearest neighborhood for estimating population diversity. According to this estimation, the mutation rate is adaptively given and repeat chromosomes are discarded over evolution. Consequently, the proposed cluster-based GA can choose a suitable mutation number for reducing computing time and maintain the population variety for preventing premature convergence. It is confirmed in numerical optimization simulations that the proposed GA is superior than traditional GA used fixed mutation rate in terms of accuracy, computing time and convergent speed
Determining Strength of Muscle Contraction Using Electromyogram

The purpose of this study is to detect forearm muscle contraction strength through use of electromyographic signals. Using a C coding software program, an algorithm is established to first detect an electromyographic signal and then to determine the strength of contraction of the muscle. A PIC microprocessor and printed circuit board (PCB) are also used to implement this task. Specifically, the amplitude of each contraction is studied to determine a weak, medium, or strong contraction type. This system can be used towards making objects (such as a wheelchair) move with different accelerations based on the strength of contraction of some muscle on the body.
Fuzzy adaptive mechanism for improving the efficiency and precision of vision-based automatic guided vehicle control

This paper proposes a vision-based automatic guided vehicle hybrid control system, which incorporated fuzzy adaptive mechanism with dynamic motion model, for improving the efficiency and precision of vehicle guidance trajectory. Vehicle is guiding along the lane center and keeps correct attitude. In this paper, vehicle's attitude is obtained from the turning angle of front wheel (/spl delta/) which is derived by a vision-based vehicle dynamic behavior acquisition subsystem (VVDBA) in our previous work. We define /spl Delta//spl delta/ and threshold value in this paper. If /spl Delta//spl delta/ is under the threshold, vehicle is guided by dynamic motion model. Otherwise, the fuzzy inference system is used to handle the vehicle's speed, so that it can be moving smoothly and avoiding exceed out the lane boundary. The hybrid strategy was simulated by MATLAB in this paper, and it would obtain robust and precise tracking capabilities along any arbitrary lane.
Application of least square method in intelligent selection of refrigerating compressors

The traditional methods used to select refrigerating compressors have errors, such as consulting values from charts or estimating values subjectively. It leads to selecting the models of compressors inaccurately. During the course of model selection for compressor, too many parameters are concerned, which leads to a large amount of work and calculation. Method of minimum squares can be used to imitate the performance curves of compressor. It makes selecting compressor without chart and only concerned with condensation temperature and evaporation temperature, which greatly improves the speed and quality of selecting compressor.
Dc-motor multipath tracking control

Based on STC89C52 single-chip microcomputer as control core, according to the infrared sensor readings judge the path ahead. Using the PWM speed regulation and incremental PID algorithm control dc-motor speed to achieve the purpose of the intelligent vehicle fast steering and tracking. To establish the mathematical model of dc-motor, for parameters setting of incremental PID control strategy based on Matlab/Simulink. The results show that: through to the parameters setting, the intelligent vehicle in different paths can achieve good control effect.
A multiscale modeling and experimental study of underfill flow and void formation for flip-chip packages

This paper uses a validated, two-dimensional global underfill flow model previously developed by the authors  to examine the effects of substrate surface (ceramic vs. organic) and temperature-dependent underfill viscosity on underfill flow-out time, flow front shape, and void formation during the flip chip encapsulation process. Model predictions are validated by experiments using bumped quartz dies that allow for direct visualization of the underfill infiltration process. In addition, a full three-dimensional underfill flow model is developed to quantify the effect of slanted and convex-shaped solder bumps. The filler particle inhomogeneity due to settling and shear migration is also accounted for in the model. The present study seeks to provide a comprehensive understanding of concurrent effects in the flip-chip underfilling process.
Unstructured road detection and path tracking for tracked mobile robot

In this paper, we propose an unstructured road navigation system to achieve the visual navigation for tracked mobile robot. The environment information and unstructured road information are obtained through the understanding of sensor data. Then the robot can be automatically controlled to track along the path. We have two contributions. Firstly, we have improved the efficiency of the algorithm for detecting unstructured road and achieve the real-time requirements. We achieve this by down-sampling the pictures and making optimization of a local soft voting method which is used to search for road vanish points. Secondly, we propose the using of the velocities of two wheels to control the robot rather than using velocity and angular velocity, which is generally utilized in most motion control systems but is difficult to control in practice. The road regional center is regarded as the preview points to imitate artificial driving behavior and the velocities of two wheels are exported respectively. Then the robot is controlled to track the available path using fuzzy control technology. Simulation and experiment results validate the efficiency, accuracy and practicality of this proposed system.
A modality synthesis framework: Using patch based intensity histogram and Weber Local Descriptor features

Image modality synthesis has attracted much attention recently. In this paper, we propose a general framework for modality synthesis, which utilizes features such as the intensity histogram and the Weber Local Descriptor. A novel distance measure is proposed to search the most similar patch from the database. After identifying the corresponding patch in the database, modality synthesis becomes straightforward in that we just have to obtain the intensity from the corresponding patch of the targeting modality to fill in the corresponding location. Database reduction and a spatio population constraint are applied to confine the search space, thus reducing the computational time. We apply the proposed modality synthesis framework to a synthetic image study and an MRI synthesis application to demonstrate its effectiveness. A comparison with a state-of-the-art modality synthesis method shows that our method is superior in terms of synthesis quality.
Design and research of RS232 communication circuit used for 0.6um CMOS sensor IC

A RS232 transmitter interface circuit used for CMOS sensor IC has been developed in a 0.6mum CMOS technology, which makes it possible to integrate CMOS sensors and subsequent signal processing and communication interface circuits in a single chip. The positive and negative voltages (+7.6V/-6.1V under +5V single supply) are achieved with on-chip charge pump, which are used as supply of transmitter circuit. The transmitter acts as level and logic transfer unit and converts TTL/CMOS level to EIA/TIA RS232 level at 230.4kbps data rate under 3KOmega and 1000pF loads
Mass detection using tolerance intervals and a rank detector [mammography application]

An adaptive, multiscale method for mass detection using a circular ring template and a nonparametric test is presented. Training data corresponding to the local background intensity level is extracted from the outer ring of the template. Test data for mass detection is obtained from the inner disk. The statistical nature of the background, specifically the tail structure of the unknown distribution, is estimated via tolerance intervals. Mass detection is based on the fraction of test data within the tail. Empirical Receiver Operating Characteristic (ROC) curves show a Sensitivity of 1 and a False Positive Rate of 1.40 per image on 30 images.
Modeling of Biochemical Pathways: Physical Interpretation of Network Behavior

The development of computational models allows one to easily reveal certain behaviors of the biochemical network of interest. In this project we seek to represent the set of rate equations used to describe the dynamics of a system with a common physical model. This allows for the reduction of such complexities that can restrict the model's overall stability. Conclusions from preliminary data have shown that the model produces valid results consistent with experimental data. We also have tested the model's reliability by reversing the reaction's driving potential. It has been concluded that this modeling approach gives logical and consistent results. The next phase of this project would be to apply this approach to a larger biochemical process, such as glycolysis.
Joint 2-D Angle and Delay Estimation for Multipath Channel Using Fourth-Order Cumulants

In the paper, fourth-order cumulant is applied in joint two-dimensional (2-D) angle and delay estimation in order to suppress Gaussian colored noise and improve resolution for wireless multipath channel. Based on a special plane antenna array, a novel TST-cumulant method is proposed to joint estimation multipath channel parameters of azimuth, elevation and time delay. The proposed algorithm is based on the eigenstructures of the spatial cumulant 2-D DOA matrix and the temporal cumulant ESPRIT, respectively. With super-resolution, the proposed method effectively estimates close 2-D angle or delay without any spectrum peak searching, working in additive unknown spatially and temporally colored Gaussian noises in wireless channel. Based on the obtained information of estimated parameters, three-dimensional localization can be realized. The simulation results of the proposed algorithm are given.
Characterization of breathing sounds based on a silicone head model with airway obstruction

Abnormal breathing sounds have been related to obstructive sleep apnea (OSA). This project aims at the development of a life-size upper airway model for studying the breathing sounds associated with obstructions. A silicone head model was built and connected to a ventilator. With obstructions of different degrees created at different locations inside the airway, the airflow sounds recorded with an electronic stethoscope were analyzed. The frequency spectra of the various recordings showed a certain degree of variation. Obstructions at the pharynx consistently showed an increased level of acoustic intensity (+7~26%) compared to the control (unobstructed). The model established in this project should help further studies on acoustics-based diagnoses of OSA.
Morphological templates for extracting texture information in X-ray mammography

Describes a new filter to enhance lesions in X-ray mammograms by extracting texture information within a morphological template. A circular ring template is used to characterize the morphological features for certain types of lesions. Three texture-related features (standard deviation, skew and kurtosis) are applied within the template for visual enhancement of the lesions. The results showed that the ring template enhanced visualization of a typical lesion by creating a "halo" response surrounding the lesion. The standard deviation, skew and kurtosis were sensitive to roughness, the presence of peaks and abrupt changes in the texture of the mammograms. Of the 45 lesions, 87% were enhanced using standard deviation, 84% by skew and 80% by kurtosis.
Californica dreaming: neurotransmitter 'doze' response and neuronal signal analysis of aplysia californica

The invertebrate central nervous system is ideally suited for the study of neurophysiology, as the comparative size of the individual neuron is rather large; on the order of 20 to 200 microns. Intracellular recordings of these cells are accomplished with a modicum of difficulty to an investigator familiar with the anatomy of the specified invertebrate. Once a microelectrode is inserted in the cell, neurotransmitters, such as 5-hydroxytryptamine (5-HT) and acetylcholine (ACh), can be released into the surrounding tissue bath, and the response to those neurotransmitters are observed as changes in the neuronal output. The engineering aspect of this investigation lies within the signal processing of the observed response. This research examines the dissection process of Aplysia californica, the cellular response of the central nervous system to 5-HT and ACh, and the signal processing techniques of the resulting waveforms.
Analysis of Water Quality Evolution and Its Influence Factor in Qingdao Inshore Area

Qingdao is a representative coastal city, whose inshore water quality is in a deterioration tendency influenced by anthropogenic activities. Recent evolution of water quality was quantitatively analyzed by adopting fuzzy comprehensive evaluation method, and its predominant influence factors were determined by a multi-regression model. The results show that: (1)Qingdao inshore water quality worsened from 1998 to 2000, became the worst in 2000, gradually improved from 2000 to 2002, but presented a slight deterioration tendency from 2002. (2)Total discharge amount of waste water, pollutant discharge amount, environmental protection investment and industrial structure were the four remarkable factors affecting Qingdao inshore water quality, among which pollutant discharge amount played the most important role. It is the key of improving water quality to reduce the pollutant discharge amount.
Classification Of Digital Angiograms Using Artificial Neural Networks

In this paper, the primary results of using neural networks for classification of digital angiograms are presented. A multilayer perceptmn was implemented and trained to identify the artery from its background. The network was trained by a back-propagation learning algorithm. The segmentation results on a digital subtraction angiogram and a cineangiogam are demonstrated. These results are also compared with the traditional maximum likelihood classifier.
Guaranteed cost control for uncertain networked switched fuzzy time-delay systems

According to switching technique, the problem of guaranteed cost control for uncertain networked switched fuzzy time-delay systems based on Takagi-Sugeno (T-S) fuzzy model is investigated. Considering the influence of time-delay under single-packet transmission condition, a sufficient condition for the existence of the robust guaranteed cost control law is presented based on single Lyapunov function and multiple Lyapunov functions method, and the switching law is also designed to implement the asymptotic stability of fuzzy switched systems. Finally an illustrative example is used to demonstrate the effectiveness of the proposed approach.
Realization of improved HMM-based speech synthesis system

This paper focuses research on the key technology of speech synthesis system based on HMM (Hidden Markov Model). With the help of HTK and Festival, three English sentences are synthesized. To improve the effect of HMM, a definition of label sequence format is described in detail. According to the definition, the results of this system are obtained. From the waveforms of the synthesized speech and the listening test, the synthesized speech is clear, understandable and naturally.