bluuebunny/crossref_metadata_2025_split huggingface dataset:
Identification of vehicle axle loads based on visual measurement
Authors: Liu Yingkai , Wang Wei , Deng Lu , Dai Jianjun , He Wei 
Published: June 2022

Abstract
Monitoring vehicle axle loads is very important for preventing infrastructure degradation and traffic accidents. However, developing an accurate, cost-effective, and easy-to-implement axle load identification technology still remains challenging. In this study, a vehicle axle load identification method based on visual measurement is proposed. The principle of the method is that each axle load of the vehicle is determined based on the vehicle unsprung masses, the ratio of vehicular sprung mass to unsprung mass, and the centroid position of the sprung mass. While the vehicle unsprung masses can be treated as a known with the vehicle model identified, the mass ratio can be predicted from the visually captured free damped vibrations of the vehicle as it passes over a speed bump. To be more specific, the vibration responses of a vehicle passing over a speed bump are firstly measured by a camera, and the vehicle system matrix, which is composed of the mass matrix, stiffness matrix, and damping matrix, can be obtained from the vehicle responses. The axle load can then be determined based on the element ratios in the system matrix of the vehicle whose unsprung masses are known. The performance of the proposed method was evaluated by numerical simulations and field tests. The results show that the errors of the simulation results are all less than 1% under ideal conditions. In the field test, the average error in identifying axle loads at different vehicle speeds was 7.01% when the vehicle speed was below 20 m s−1, which proved the accuracy and effectiveness of the proposed method. This study has demonstrated a novel application of computer vision technology to identify the axle loads of moving vehicles. The proposed method does not require installation of sensors on the roadway or the vehicle, making it a promising alternative for traditional weigh-in-motion systems.
Revealing charge carrier dynamics and transport in Te-doped GaAsSb and GaAsSbN nanowires by correlating ultrafast terahertz spectroscopy and optoelectronic characterization
Authors: Yuan Long , Pokharel Rabin , Devkota Shisir , Kuchoor Hirandeep , Dawkins Kendall , Lee Min-Cheol , Huang Yue , Yarotski Dzmitry , Iyer Shanthi , Prasankumar Rohit P 
Published: June 2022

Abstract
Recent advances in the growth of III-V semiconductor nanowires (NWs) hold great promise for nanoscale optoelectronic device applications. It is established that a small amount of nitrogen (N) incorporation in III-V semiconductor NWs can effectively red-shift their wavelength of operation and tailor their electronic properties for specific applications. However, understanding the impact of N incorporation on non-equilibrium charge carrier dynamics and transport in semiconducting NWs is critical in achieving efficient semiconducting NW devices. In this work, ultrafast optical pump-terahertz probe spectroscopy has been used to study non-equilibrium carrier dynamics and transport in Te-doped GaAsSb and dilute nitride GaAsSbN NWs, with the goal of correlating these results with electrical characterization of their equilibrium photo-response under bias and low-frequency noise characteristics. Nitrogen incorporation in GaAsSb NWs led to a significant increase in the carrier scattering rate, resulting in a severe reduction in carrier mobility. Carrier recombination lifetimes of 33 ± 1 picoseconds (ps) and 147 ± 3 ps in GaAsSbN and GaAsSb NWs, respectively, were measured. The reduction in the carrier lifetime and photoinduced optical conductivities are due to the presence of N-induced defects, leading to deterioration in the electrical and optical characteristics of dilute nitride NWs relative to the non-nitride NWs. Finally, we observed a very fast rise time of ∼2 ps for both NW materials, directly impacting their potential use as high-speed photodetectors.
Machine learning for multi-parametric breast MRI: radiomics-based approaches for lesion classification
Authors: Altabella Luisa , Benetti Giulio , Camera Lucia , Cardano Giuseppe , Montemezzi Stefania , Cavedon Carlo 
Published: June 2022

Abstract
In the artificial intelligence era, machine learning (ML) techniques have gained more and more importance in the advanced analysis of medical images in several fields of modern medicine. Radiomics extracts a huge number of medical imaging features revealing key components of tumor phenotype that can be linked to genomic pathways. The multi-dimensional nature of radiomics requires highly accurate and reliable machine-learning methods to create predictive models for classification or therapy response assessment.
Multi-parametric breast magnetic resonance imaging (MRI) is routinely used for dense breast imaging as well for screening in high-risk patients and has shown its potential to improve clinical diagnosis of breast cancer. For this reason, the application of ML techniques to breast MRI, in particular to multi-parametric imaging, is rapidly expanding and enhancing both diagnostic and prognostic power. In this review we will focus on the recent literature related to the use of ML in multi-parametric breast MRI for tumor classification and differentiation of molecular subtypes. Indeed, at present, different models and approaches have been employed for this task, requiring a detailed description of the advantages and drawbacks of each technique and a general overview of their performances.
Effects of phase aberration on transabdominal focusing for a large aperture, low f-number histotripsy transducer
Authors: Yeats Ellen , Gupta Dinank , Xu Zhen , Hall Timothy L 
Published: June 2022

Abstract

Objective. Soft tissue phase aberration may be particularly severe for histotripsy due to large aperture and low f-number transducer geometries. This study investigated how phase aberration from human abdominal tissue affects focusing of a large, strongly curved histotripsy transducer. Approach. A computational model (k-Wave) was experimentally validated with ex vivo porcine abdominal tissue and used to simulate focusing a histotripsy transducer (radius: 14.2 cm, f-number: 0.62, central frequency f
c: 750 kHz) through the human abdomen. Abdominal computed tomography images from 10 human subjects were segmented to create three-dimensional acoustic property maps. Simulations were performed focusing at 3 target locations in the liver of each subject with ideal phase correction, without phase correction, and after separately matching the sound speed of water and fat to non-fat soft tissue. Main results. Experimental validation in porcine abdominal tissue showed that simulated and measured arrival time differences agreed well (average error, ∼0.10 acoustic cycles at f
c). In simulations with human tissue, aberration created arrival time differences of 0.65 μs (∼0.5 cycles) at the target and shifted the focus from the target by 6.8 mm (6.4 mm pre-focally along depth direction), on average. Ideal phase correction increased maximum pressure amplitude by 95%, on average. Matching the sound speed of water and fat to non-fat soft tissue decreased the average pre-focal shift by 3.6 and 0.5 mm and increased pressure amplitude by 2% and 69%, respectively. Significance. Soft tissue phase aberration of large aperture, low f-number histotripsy transducers is substantial despite low therapeutic frequencies. Phase correction could potentially recover substantial pressure amplitude for transabdominal histotripsy. Additionally, different heterogeneity sources distinctly affect focusing quality. The water path strongly affects the focal shift, while irregular tissue boundaries (e.g. fat) dominate pressure loss.
A 3-DOF inertial impact locomotion robot constructed on four piezoelectric bimorph actuators
Authors: Li Jianxing , Zhang Shijing , Liu Yingxiang , Deng Jie , Ma Xuefeng 
Published: June 2022

Abstract
An inertial impact locomotion robot (IILR), consisting of a base, three supporting feet and four vertically distributed piezoelectric bimorph actuators (PBAs), was developed in this work. The IILR could complete plane locomotion with three degrees of freedom (DOFs), including translation along X-axis, translation along Y-axis and rotation around Z-axis. The inertial impact force driving the IILR was generated by the PBAs through vibration. A simplified dynamic model of the IILR was established to predict its locomotion characteristics. Then, a prototype was fabricated, whose size was Φ114 × 14 mm3 and weight was 160.5 g. Then the locomotion performances and carrying capability of the IILR were tested, the results indicated that the maximum linear velocity was 76.66 μm s−1 and the maximum rotary velocity was 161.19 μrad s−1 under voltage of 600 Vp-p and frequency of 9 Hz, respectively. In addition, the maximum carrying load was 1600 g (about 9.97 times of self-weight). The experiment results were also in good agreement with the dynamics simulation results. Overall, the IILR held the characteristics of multiple DOFs, large carrying capability, simple structure and no electromagnetic interference; therefore, it could be suitable for precision positioning and carrying applications such as wafer inspection, microscopic observation and operation.
Phase control of optical Goos–Hänchen shifts in a quantum dot nanostructure via high refractive index
Authors: Raheli Ali , Abdulkareem Sarkew S , Al-Qargholi Basim 
Published: June 2022

Abstract
We proposed a model for adjusting Goos–Hänchen (GH) shifts in a cavity with quantum dot (QD) nanostructure in this letter. The actual component of the susceptibility was studied by analytical solution of the coherence term of the density matrix elements, and the refractive index of the QD nanostructure was explored. We discovered that the intracavity medium became phase sensitive because of the electron tunneling action. As a result, the relative phase of applied lights may be used to manipulate the medium’s refraction index. The GH shifts in reflected and transmitted light beams in high refractive index QD nanostructures with diminishing probe absorption were next examined. We discovered that the GH shifts of reflected and transmitted lights are greatly influenced by the applied lights’ relative phase. We established that greater negative or positive GH shifts in reflected and transmitted photons are conceivable in the presence of electron tunneling.
Characterizing quantum nonlocalities under the Heisenberg XYZ spin model with Dzyaloshinskii–Moriya interaction
Authors: Xing Ling-Ling , Yang Huan , Xu Zong , Hu Xun-Mei , Li Ze-Bin , Kong Min , Zhang Gang 
Published: June 2022

Abstract
In quantum information science, the nontrivial applications of the Heisenberg spin model are to realize quantum communication and quantum computing using the quantum nonlocalities between particles in the spin chain. Here, considering Heisenberg XYZ spin model with Dzyaloshinskii–Moriya (DM) interaction, our attentions are directed to ascertain the nonlocal advantage of quantum coherence (NAQC), and also characterize the Bell nonlocality (BN). As revealed from the results, one can use low temperature to realize situations in which the NAQC and BN are invariant. The NAQC and BN cannot be detected if temperature is high. External temperature strongly influences the two quantum nonlocalities in ferromagnetic systems. The strong coupling parameter

ℑ
x

brings on the fact that the two quantum nonlocalities are invariant. It is very difficult to capture the NAQC and BN if

ℑ
x

is weak. Considering

ℑ
y

>
0

, the strong

ℑ
y

is responsible for freezing quantum nonlocalities, and one cannot witness the NAQC when

ℑ
y

is low. Moreover, the freezing of quantum nonlocalities can be achieved via enhancing

ℑ
z

, and the detection of NAQC is difficult if

ℑ
z

is weak. Of particular note, under the influence of DM interactions, NAQC (BN) cannot (can) be frozen both in antiferromagnetic and ferromagnetic systems. The strong

D
y

and

D
z

give rise to the difficulty of capturing the NAQC.
Detection of 16 Small Glitches in Nine Pulsars
Authors: Zhou Zu-Rong , Wang Jing-Bo , Wang Na , Yuan Jian-Ping , Kou Fei-Fei , Dang Shi-Jun 
Published: June 2022

Abstract
Pulsar timing measurements with a 26 m radio telescope at Nanshan between 2000 and 2014 were used to search for glitch events. The data span of nine pulsars ranges from 11.6 to 14.2 yr, and 16 new glitch events were identified in nine pulsars. Glitch parameters were determined through fitting the timing residuals data. All 16 glitches have a small fractional size. Six new glitches have been detected in PSR J1833−0827, making it another frequent glitching pulsar. Some of the 16 glitches may experience exponential or linear recovery, but it is unlikely for us to make further analyses with the large gap in the data set. All the glitch rates obtained from Nanshan are higher than that from Jodrell Bank Observatory. The small glitch size and high glitch rate could possibly attribute to the high observation cadence.
Adaptive binary multi-objective harmony search algorithm for channel selection and cross-subject generalization in motor imagery-based BCI
Authors: Shi Bin , Yue Zan , Yin Shuai , Wang Weizhen , Yu Haoyong , Huang Zhen , Wang Jing 
Published: June 2022

Abstract

Objective. Multi-channel electroencephalogram data containing redundant information and noise may result in low classification accuracy and high computational complexity, which limits the practicality of motor imagery (MI)-based brain-computer interface (BCI) systems. Therefore, channel selection can improve BCI performance and contribute to user convenience. Additionally, cross-subject generalization is a key topic in the channel selection of MI-based BCI. Approach. In this study, an adaptive binary multi-objective harmony search (ABMOHS) algorithm is proposed to select the optimal set of channels. Furthermore, a new adaptive cross-subject generalization model (ACGM) is proposed. Three public MI datasets were used to validate the effectiveness of the proposed method. Main results. The Wilcoxon signed-rank test was performed on the test accuracies, and the results indicated that the ABMOHS method significantly outperformed all channels (p< 0.001), the C3–Cz–C4 channels (p< 0.001), and 20 channels (p< 0.001) in the sensorimotor cortex. The ABMOHS algorithm based on Fisher’s linear discriminant analysis (FLDA) and support vector machine (SVM) classifiers greatly reduces the number of selected channels, especially for larger channel sizes (Dataset 2), and obtains a comparative classification performance. Although there was no significant difference in test classification performance between ABMOHS and non-dominated sorting genetic algorithm II (NSGA-II) when FLDA and SVM were used, ABMOHS required less computational time than NSGA-II. Furthermore, the number of channels obtained by ABMOHS algorithm were significantly smaller than those obtained by common spatial pattern-Rank and correlation-based channel selection algorithm. Additionally, the generalization of ACGM to untrained subjects shows that the mean test classification accuracy of ACGM created by a small sample of trained subjects is significantly better than that of Special-16 and Special-32. Significance. The proposed method can reduce the calibration time in the training phase and improve the practicability of MI-BCI.
Tutorial: a guide to techniques for analysing recordings from the peripheral nervous system
Authors: Koh Ryan G L , Zariffa José , Jabban Leen , Yen Shih-Cheng , Donaldson Nick , Metcalfe Benjamin W 
Published: June 2022

Abstract
The nervous system, through a combination of conscious and automatic processes, enables the regulation of the body and its interactions with the environment. The peripheral nervous system is an excellent target for technologies that seek to modulate, restore or enhance these abilities as it carries sensory and motor information that most directly relates to a target organ or function. However, many applications require a combination of both an effective peripheral nerve interface (PNI) and effective signal processing techniques to provide selective and stable recordings. While there are many reviews on the design of PNIs, reviews of data analysis techniques and translational considerations are limited. Thus, this tutorial aims to support new and existing researchers in the understanding of the general guiding principles, and introduces a taxonomy for electrode configurations, techniques and translational models to consider.
Occupation time of a renewal process coupled to a discrete Markov chain
Authors: Dessertaine Théo , Godrèche Claude , Bouchaud Jean-Philippe 
Published: June 2022

Abstract
A semi-Markov process is one that changes states in accordance with a Markov chain but takes a random amount of time between changes. We consider the generalisation to semi-Markov processes of the classical Lamperti law for the occupation time of a two-state Markov process. We provide an explicit expression in Laplace space for the distribution of an arbitrary linear combination of the occupation times in the various states of the process. We discuss several consequences of this result. In particular, we infer the limiting distribution of this quantity rescaled by time in the long-time scaling regime, as well as the finite-time corrections to its moments.
Power grid fault classification method of alarm information based on bigru-attention.
Authors: Zhang Fengming , Zhang Xinxin , Yang Xiaofeng , Liu Jiang , Wang Yi 
Published: June 2022

Abstract
With the continuous expansion of the power grid, the number of alarm information collected by the dispatching center is also increasing. How to filter out key information from massive alarm information, delete irrelevant data, classify the importance of alarm information, and make preparations for power grid fault diagnosis based on alarm information has become an urgent problem to be solved in online fault diagnosis. Based on this, this paper proposes an importance classification method of alarm information based on bigru attention to screen the data with the strongest correlation with fault, avoid the impact of irrelevant data on fault diagnosis, and meet the needs of intelligence. Firstly, the two-way gated loop (bigru) neural network layer is used to preprocess the alarm information text and extract the features of the deep-seated information; Secondly, the attention mechanism layer is used to assign corresponding weights to the extracted text deep-seated information; Finally, the text feature information of alarm information with different weights is put into the softmax function layer for alarm information text classification. Finally, an actual fault case in an area is tested to verify the effectiveness and practicability of the proposed method.
Thermal analysis method of optical scanning system drive shaft system
Authors: Weining Chen , Gong Cheng , Sansan Chang , Bo Gao , Xiang Li 
Published: June 2022

Abstract
In order to improve the heating problem of the rotating shaft components of the optical surveillance scanning system due to continuous operation, the feasibility of applying the oil-air lubrication technology to the drive shaft system of the optical surveillance scanning system was studied. Analyzed the contact load of the spindle bearing, obtained the calorific value of the heat source of the spindle component of the drive system, established the finite element analysis model of the temperature field of the spindle component, and calculated the thermal stability of the spindle component under the conditions of no lubrication and lubrication. Temperature rise. It is proposed to apply the oil-air lubrication method to the drive shaft system of the optical monitoring scanning system, and the corresponding lubrication parameters are calculated.
Overtemperature fault diagnosis of front bearing for main spindle based on CNN + LSTM
Authors: Wei Wang , Ruiqing Kang , Yu Zhang 
Published: June 2022

Abstract
The main spindle is an important transmission component of the wind turbine. The overtemperature fault of the front bearing of the main spindle is caused due to mechanical wear, grease failure and other reasons. A neural network based on convolutional neural networks (CNN) and long short memory network is built (LSTM) to judge the early fault. Method used in this paper can find the fault in advance. Compared with BP neural network, support vector machine, the accuracy of the model used in this paper is higher, which is up to 99.77%. The mechanism model of spindle operation will be established to analyse the manifestations of various faults and improve the accuracy in the future.
Design of cryogenic long-wave infrared detection system
Authors: Hongwei Zhang , Weining Chen , Rui Qu , Yingjun Ma , Yalin Ding , Haifeng Xu 
Published: June 2022

Abstract
To address the demand for detection of point / dim targets in complex environments, a cryogenic long-wave infrared detection system was designed. In order to improve the target detection capability, the system adopts high-order aspheric surfaces to reduce the number of optical lenses and improve the system transmittance. At the same time, it corrects on-axis / off-axis aberrations and advanced aberrations to improve the imaging quality of the system. In order to effectively reduce the background radiation and improve the system signal-to-noise ratio, the system adopts cryogenic optical technology. Through the scheme design and calculation analysis of the active refrigeration unit, the requirements of the overall and optical technical indicators are met. The outline of the aircraft image obtained by the field experiment is clear and distinguishable, which meets the requirements of target detection. The system has a good application prospect in the field of infrared imaging in early warning systems.
Analysis on the development of semiconductor manufacturing process
Authors: He Zihan 
Published: June 2022

Abstract
The computing speed of chips depends on the number of transistors. In other words, if you have more transistors, the computing speed of chips will be faster. Supposing the area of a chip is limited, we need to develop ways to fit in more transistors. To put more transistors in chips, minimizing the existing microelectronic device is a good way. To achieve that goal, we need to develop effective semiconductor manufacturing process to develop the performance of chips. In this paper, the research topic is to introduce the development of processes and also show their relevant applications of process. This paper intends to let more people realize the importance of semiconductor manufacturing processes and how it applies to our daily lives. This paper will investigate the following question: until October 2021, which is the most advanced manufacturing process? What are some other areas that we can expand through manufacturing process? The conclusion is that until October 2021, the most advanced process is N5P. Semiconductor manufacturing processes apply to areas like Adaptive Substatistical PCA, complicated material modeling tools and methods.
Scale like Co(OH)2/CoOOH modified platinum catalytic gas diffusion electrode and its application in ammonia detection
Authors: Dong Hanpeng , Wu Zhengwei 
Published: June 2022

Abstract
Platinum (Pt) is sputtered on porous PTFE membrane as the basic electrode. Cobalt hydroxide catalyst is deposited on the porous platinum/PTFE electrode by an electrochemical assisted deposition method with ammonia gas employed as precipitant. SEM image of the catalyst shows an integration of scale like flake of about 0.5 micrometer in diameter. The XRD analysis suggested that the catalyst is a mixture of β-Co(OH)2 and CoOOH. The mixture is regard as the effective constituent in the ammonia electrode reaction according to the electrochemical analysis. The cyclic voltammogram indicate that at higher than 0.4V (v.s. saturated HgO/Hg electrode reference) the catalytic activity of the working electrode increased significantly. With the modified electrode as working electrode and a pure platinum electrode as assistant electrode, a four electrodes sensor is established to measure ammonia in air successfully.