Jun Zhang

Anhui University · School of Electrical Engineer & Automation

Background Circular RNAs (circRNAs) are a class of noncoding RNAs that are involved in chondrogenic differentiation, and N6-methyladenosine (m⁶A) broadly exists in circRNAs. Materials and methods A joint injury model was constructed on Diannan small-ear (DSE) pigs. Transfections were constructed using Lipofectamine 2000. Real-time quantitative PCR...

Pedestrian trajectory prediction is extremely challenging due to the complex social attributes of pedestrians. Introducing latent vectors to model trajectory multimodality has become the latest mainstream solution idea. However, previous approaches have overlooked the effects of redundancy that arise from the introduction of latent vectors. Additio...

Goal: Cervical cancer is one of the most common cancers in women worldwide, ranking among the top four. Unfortunately, it is also the fourth leading cause of cancer-related deaths among women, particularly in developing countries where incidence and mortality rates are higher compared to developed nations. Colposcopy can aid in the early detection...

Cranes are pivotal heavy equipment used in the construction of transmission line scenarios. Accurately identifying these cranes and monitoring their status is pressing. The rapid development of computer vision brings new ideas to solve these challenges. Since cranes have a high aspect ratio, conventional horizontal bounding boxes contain a large nu...

Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging technology for monitoring cerebral hemodynamic responses. Enhancing fNIRS classification can improve the performance of brain–computer interfaces (BCIs). Currently, deep neural networks (DNNs) do not consider the inherent delayed hemodynamic responses of fNIRS signals, whi...

Recently, convolutional neural network (CNN) based image super-resolution (SR) methods have achieved significant performance improvement. However, most CNN-based methods mainly focus on feed-forward architecture design and neglect to explore the feedback mechanism, which usually exists in the human visual system. In this paper, we propose feedback...

The quality of concrete is crucial for the safety of facilities. Specifically, the ex-posed surface defects of the bridge seriously affect its strength and aesthetics. However, due to the influence of weather and light, different types of defects on the concrete surface may potentially overlap, making it difficult for classification algorithms to i...

In recent times, there has been notable progress in the effectiveness of Generative Adversarial Networks (GANs) for synthesizing images. Consequently, numerous studies have started utilizing GANs for image editing purposes. To enable editing of real images, it is crucial to embed a real image into the latent space of GANs. This involves obtaining t...

Printed Circuit Board (PCB) is a significant component of the power system, and their surface defects may hinder electrical performance. Therefore, developing an efficient and precise PCB surface defect detection method is crucial for ensuring the state of the entire power system. In recent years, there has been growing interest in lightweight atte...

Corneal ulcer is a common disease located in the eye. If not detected and treated in a timely manner, it is highly likely to cause irreversible damage to the patient’s eyes, and even lead to blindness. Traditional detection methods have drawbacks such as complex steps and painful inspection processes. So there is an urgent need to develop a fast, c...

Distracted driver classification () plays an important role in ensuring driving safety. Although many datasets are introduced to support the study of, most of them are small in data size and are short of diversity in environmental variations. This largely limits the development of since many practical problems such as the cross-modality setting can...

As a high mortality disease, cancer seriously affects people's life and well-being. Reliance on pathologists to assess disease progression from pathological images is inaccurate and burdensome. Computer aided diagnosis (CAD) system can effectively assist diagnosis and make more credible decisions. However, a large number of labeled medical images t...

circRNAs play an important role in the progression of osteoarthritis (OA). Therefore, we aimed to reveal the mechanism of action of circRNA-ZCCHC14 in OA. OA animal and cell models were constructed, and clinical samples were collected. The expression of circRNA-ZCCHC14 and miR-181a was detected by RT‒qPCR. The chondrogenic differentiation ability o...

Arrhythmia is an important group of cardiovascular diseases, which can suddenly attack and cause sudden death, or continue to affect the heart and cause its failure. Electrocardiogram (ECG) is an important tool for detecting arrhythmia, but its analysis is time-consuming and dependent on extensive expertise. Deep neural networks have become a popul...

Humans spend about one-third of their time in sleep. Sleep is closely related to human physical and mental health and is a very important life activity. Automatic sleep stage classification is an important tool for analyzing sleep quality. However, due to different difficulties such as poor signal quality, the minor difference between different sta...

Surface defect detection is an important part of the steel production process. Recently, attention mechanisms have been widely used in steel surface defect detection to ensure product quality. The existing attention modules cannot distinguish the difference between steel surface images and natural images. Therefore, we propose an adaptive graph cha...

Distracted driver activity recognition plays a critical role in risk aversion-particularly beneficial in intelligent transportation systems. However, most existing methods make use of only the video from a single view and the difficulty-inconsistent issue is neglected. Different from them, in this work, we propose a novel M ult I -camera F eat...

With the development of convolutional neural networks, hundreds of deep learning based dehazing methods have been proposed. In this paper, we provide a comprehensive survey on supervised, semi-supervised, and unsupervised single image dehazing. We first discuss the physical model, datasets, network modules, loss functions, and evaluation metrics th...

Edge‐device‐based object detection is crucial in many real‐world applications, such as self‐driving cars, ADAS, driver behavior analysis. Although deep learning (DL) has become the de‐facto approach for object detection, the limited computing resources of embedded devices and the large model size of current DL‐based methods increase the difficulty...

As we all know, the learning rate plays a vital role in deep neural network (DNN) training. This study introduces an incremental proportional-integral-derivative (PID) controller widely used in automatic control as a learning rate scheduler for stochastic gradient descent (SGD). To automatically calculate the current learning rate, we utilize feedb...

For deployment on an embedded processor for distracted driver classification, the model should satisfy the demand for both high accuracy, real-time inference, and limited storage resources. Conventional deep CNN models such as VGG, ResNet, DenseNet, often aim for high accuracy, making their model heavy for an embedded system with limited memory spa...

Traffic accidents caused by distracted drivers account for a large proportion of traffic accidents each year, and monitoring the driving state of drivers to avoid traffic accidents caused by distracted driving has become a very important research direction. At present, the field of driver distraction detection mainly adopts supervised learning meth...

This paper proposes a novel end-to-end pipeline that uses the ordinal information and relative relation of images for visibility estimation (VISOR-NET). By encoding ordinal information into a set of relatively ordered image pairs, VISOR-NET can learn a global ranking function effectively. Due to the lack of real scenes or continuous labels in publi...

Non-invasive blood pressure prediction is an important method to prevent diseases such as hypertension. This paper proposes a sub-network aggregation with large convolution kernel convolution to predict non-invasive blood pressure. First, the large convolution kernel module in the backbone network is used to extract PPG data features. Then, the mul...

Functional near-infrared spectroscopy (fNIRS), a non-invasive optical technique, is widely used to monitor brain activities for disease diagnosis and brain-computer interfaces (BCIs). Deep learning-based fNIRS classification faces three major barriers: limited datasets, confusing evaluation criteria, and domain barriers. We apply more appropriate e...

Precipitation nowcasting plays an important role in the early warning of disasters and many other aspects of people's lives. In this study, we address the problem of radar reflectivity image extrapolation, which has great significance for precipitation near‐range forecasting. In recent years, the related achievements of nowcasting indicate that dee...

Most conventional compressed sensing (CS) algorithms are impaired by the fact that the optimization of image reconstruction suffers from the need for multiple iterative calculations. Recently, deep learning-based CS algorithms have been proposed and they dramatically achieve efficient reconstruction and fast computing speed with fewer sampling meas...

Recently, deep learning-based compressed sensing (CS) algorithms have been reported, which remarkably achieve pleasing reconstruction quality with low computational complexity. However, the sampling process of the common deep learning-based CS methods and the conventional ones cannot sufficiently exploit the structured sparsity within image sequenc...

Putative identification of metabolites is comparing the observed mass spectrum of the sample to a reference library. However, the existing libraries cannot contain all the mass spectra due to the huge number of compounds. The identification process will fail if the target mass spectrum does not exist in the library. One solution is augmenting the l...

Functional near-infrared spectroscopy (fNIRS) is a promising neuroimaging technology. The fNIRS classification problem has always been the focus of the brain-computer interface (BCI). Inspired by the success of Transformer based on self-attention mechanism in the fields of natural language processing and computer vision, we propose an fNIRS classif...

According to the surveys of the World Health Organization, distracted driving is one of main causes of road traffic accidents. To improve road traffic safety, real-time detection of drivers’ driving behavior is very important for the development of highly reliable Advanced Driver Assistance System (ADAS). At present, the deep learning architecture...

The change of core temperature of blast furnace reflects the working status of hearth. However, the temperature of core dead stock column can not be measured by sensors directly. Therefore, a prediction model of Core Dead Stock Column Temperature is proposed in this work based on primary component analysis (PCA) and ridge regression algorithms, whe...

The presence of haze significantly reduces the quality of images. Researchers have designed a variety of algorithms for image dehazing (ID) to restore the quality of hazy images. However, there are few studies that summarize the deep learning (DL) based dehazing technologies. In this paper, we conduct a comprehensive survey on the recent proposed d...

In this paper, we tackle the few-shot learning problem in a semi-supervised setting where a limited number of labeled data-points and a number of low-cost unlabeled samples are assumed to be available. In particular, some of the unlabeled samples share the same label space with the support set, referring to as known samples, while some of them are...

Backgroud: The prediction of drug-target interactions (DTIs) is of great significance in drug development. It is time-consuming and expensive in traditional experimental methods. Machine learning can reduce the cost of prediction and is limited by the characteristics of imbalanced datasets and problems of essential feature selection. Methods: The...

The presence of haze significantly reduces the quality of images. Researchers have designed a variety of algorithms for image dehazing (ID) to restore the quality of hazy images. However, there are few studies that summarize the deep learning (DL) based dehazing technologies. In this paper, we conduct a comprehensive survey on the recent proposed d...

Graph convolutional networks (GCNs) have achieved remarkable performance on skeleton-based action recognition. Existing GCN-based methods usually apply the fixed graph topology and one fixed temporal convolution kernel to extract the spatial features of joints and temporal features, which is from a single-scale perspective. Actually, human actions...

Compound identification in electron-ionization mass spectrometry (EI-MS) is usually achieved by matching the query mass spectrum to the well-collected reference spectral library. Although various similarity methods have been developed in recent years, it is still difficult to distinguish some similar mass spectra, especially for isomers. In this wo...

The wheat mite always causes major damage in wheat plants and results in significant yield losses. Therefore, detecting wheat mites can provide important information, such as pest population dynamics and integrated pest management by monitoring wheat mite populations. However, the automatic classification and counting of wheat mites from images tak...

Papers published in top conferences or journals is an important measure of the innovation ability of institutions, and ranking paper acceptance rate can be helpful for evaluating affiliation potential in academic research. Most studies only focus on the paper quality itself, and apply simple statistical data to estimate the contribution of institut...

The data of Internet of Vehicles (IoV) can be used to evaluate the driving safety risk of auto insurance policyholder and provide technical means for Usage Based Insurance (UBI). There are many types of IoV data, such as continuous or ordinal, categorical, binary etc., which contain highly sparse and dimensional features after One-Hot processing, t...

Distracted driving behavior has become a leading cause of vehicle crashes. This paper proposes a data augmentation method for distracted driving detection based on the driving operation area. First, the class activation mapping method is used to show the key feature areas of driving behavior analysis, and then the driving operation areas are detect...

The occurrence of crop pests and diseases always affects the development of agriculture seriously, while pest meteorology showed that climate is important in affecting the occurrence. Recently, recurrent neural network (RNN) has been broadly applied in various fields, which was designed for modeling sequential data and has been testified to be quit...

The task of drug-target interaction (DTI) prediction plays important roles in drug development. The experimental methods in DTIs are time-consuming, expensive and challenging. To solve these problems, machine learning-based methods are introduced, which are restricted by effective feature extraction and negative sampling. In this work, features wit...

Few-shot learning is one of the most challenging problems in computer vision due to the difficulty of sample collection in many real-world applications. It aims at classifying a sample when the number of training samples for each identity is limited. Most of the existing few-shot learning models learn a distance metric with pairwise or triplet cons...

Background: Helicobacter pylori bacterium is a major cause of gastritis. With increasing use of antibiotics to treat infections, mutation resistant strains have emerged in most human populations. To effectively treat patients to help resolve infections, the clinician needs information on the antibiotic susceptibility profile of the infection. Ther...

Scab, frogeye spot, and cedar rust are three common types of apple leaf diseases, and the rapid diagnosis and accurate identification of them play an important role in the development of apple production. In this work, an improved model based on VGG16 is proposed to identify apple leaf diseases, in which the global average poling layer is used to r...

Fog is the main weather phenomenon that causes low visibility, which makes traffic and outdoor work extremely dangerous. In this paper, we propose a novel LSTM framework for short-term fog forecasting. The proposed network framework consists of an LSTM network and fully connected layer. In order to make the proposed LSTM framework work, the meteoro...

The study of protein-protein interaction is of great biological significance, and the prediction of protein-protein interaction sites can promote the understanding of cell biological activity and will be helpful for drug development. However, uneven distribution between interaction and non-interaction sites is common because only a small number of...

Predicting traffic speed accurately is a very challenging task of the intelligent traffic system (ITS), due to the complex and dynamic spatial-temporal dependencies from both temporal and spatial aspects. There not only exits short-term local neighboring fluctuation and long-term global trend in temporal aspect, but also local and global correlatio...

Background: The occurrence of cotton pests and diseases has always been an important factor affecting the total cotton production. Cotton has a great dependence on environmental factors during its growth, especially climate change. In recent years, machine learning and especially deep learning methods have been widely used in many fields and have...

Background: The recognition of protein interaction sites is of great significance in many biological processes, signaling pathways and drug designs. However, most sites on protein sequences cannot be defined as interface or non-interface sites because only a small part of protein interactions had been identified, which will cause the lack of predi...

Background: Accurate identification of potential interactions between drugs and protein targets is a critical step to accelerate drug discovery. Despite many relative experimental researches have been done in the past decades, detecting drug-target interactions (DTIs) remains to be extremely resource-intensive and time-consuming. Therefore, many c...

Objective: To study the therapeutic effect and mechanism of action of quercetin in a rat model of osteoarthritis (OA). Methods: The OA rat model was established by intra-articular injection of papain. Changes in knee diameter, toe volume and histopathology were measured. Levels of interleukin (IL)-β and tumor necrosis factor (TNF)-α were assesse...

Background Non-concentric reduction of hip posterior dislocation caused by the acetabular labrum rim fracture is rare. There has been very little study on the feasibility of arthroscopically treatment and medium and mid-term evaluation to this pathology. The objectives of the current study were: (1) Is the arthroscopically-assisted technique feasib...

Background: Nonconcentric reduction of hip posterior dislocation caused by the acetabular labrum rim fracture is rare. There has been very little study on the feasibility of arthroscopically treatment and medium and mid-term evaluation to this pathology. The objectives of the current study were: (1) Is the arthroscopically assisted technique feasi...

The forecasting of traffic flow is an important part of intelligent transportation system; actual and accurate forecasting of traffic flow can give scientific support for urban traffic guidance and control. As there is big forecast error when modeling toward traffic flow data with discrete grey model DGM (1, 1), this paper amends the equal interval...

Driving behavior recognition is a challenging task that exploits the acceleration and angular velocity information of the vehicle collected by smartphone to identify various driving events. Traditional methods usually extract hand-crafted features from raw data, leading to under-explored temporal features of driving behaviors. To address the issue...

Apple tree disease is a main threat factor to apple quality and yield. This paper proposed an improved convolutional neural network model to classify apple tree diseases. It took the advantages of neural network to extract the deep characteristics of disease parts, and used deep learning to classify target disease areas. In order to improve the cla...

Hearth activity is one of the most important factors which affect the smooth progress of production and even the life of blast furnace. However, the calculation of hearth activity depends on the empirical model entirely, and the model parameter acquisition is difficult. To overcome this deficiency, this paper presents a novel method based on an imp...

Accurate identification of apple leaf diseases is key to the prevention and control of insect pests and diseases in apple trees. This paper proposed an improved convolutional neural network combining batch normalization and center loss function based on VGG16 model. Batch normalization is used to normalize the input data of the convolutional layer,...

Particle urinary sediment analysis in microscopic images can help doctors assess patients with kidney and urinary tract disease. Manual urine sediment inspection is labor intensive, subjective and time consuming, and traditional automated algorithms often extract handcrafted identification features. In this paper, instead of using manual extraction...

Regarding the fierce competition between research institutions, institutional rankings are widely carried out. At present, there are many factors affecting the ranking of institutions, but most of them are aimed at the attributes of the institutions themselves, and the feature selection is relatively simple. Therefore, this paper proposes a state-o...

Pedestrian detection is an essential technology in robotics, intelligent transportation system, and intelligent video surveillance. Pedestrians in the surveillance scene have the characteristics of dense crowds and high degree of occlusion, meanwhile, it needs to meet the requirements of real-time detection. To solve this problem, the method based...

We propose a new regularization strategy called DropMI, which is a generalization of Dropout for the regularization of networks that introduces mutual information (MI) dynamic analysis. The standard Dropout randomly drops a certain proportion of neural units, according to the Bernoulli distribution, thereby resulting in the loss of some important h...

Hashing is one of the most popular image retrieval technique since its fast-computational speed and low storage cost. Recently, deep hashing methods have greatly improved the image retrieval performance in contrast to traditional hashing method. However, the binary hashing representation is only generated from the global image region, which may res...

Objective: To investigate the effectiveness of arthroscopic treatment for irreducible hip posterior dislocation caused by acetabular labrum bony Bankart lesions. Methods: Between February 2008 and August 2016, 11 patients with irreducible hip posterior dislocation caused by acetabular labrum bony Bankart lesions, were treated with arthroscopic r...

Human driving behaviors are personalized and unique, and the automobile fingerprint of drivers could be helpful to automatically identify different driving behaviors and further be applied in fields such as auto-theft systems. Current research suggests that in-vehicle Controller Area Network-BUS (CAN-BUS) data can be used as an effective representa...

A large number of studies have shown that most vehicle collisions are caused by drivers’ abnormal operations. To ensure the safety of all people on the road network as much as possible, it is crucial to be able to predict the drivers’ driving safety risks in real time. In this paper, we propose a novel cost-sensitive $L1/L2$ -nonnegativity-constr...

Development of a sophisticated nanotherapeutic platform to deliver potent agents effectively and safely to desired tumor sites remains challenging. Cabazitaxel (CTX) holds particular interest for clinical use because of its ability to overcome the drug resistance caused by other taxane drugs. However, investigations of this potent agent have been m...

Shift-scheduling calibration is important to the automobile industry, but it is repetitive and time-consuming; it is thus desirable to have a robot driver to automate this process. In this paper, we propose automating the calibration of shift-scheduling by using bionic optimization, i.e., particle swarm optimization (PSO), to guide the searching pr...

Background: Hot spot residues are functional sites in protein interaction interfaces. The identification of hot spot residues is time-consuming and laborious using experimental methods. In order to address the issue, many computational methods have been developed to predict hot spot residues. Moreover, most prediction methods are based on structur...

Regarding the growth of crops, one of the important factors affecting crop yield is insect disasters. Since most insect species are extremely similar, insect detection on field crops, such as rice, soybean and other crops, is more challenging than generic object detection. Presently, distinguishing insects in crop fields mainly relies on manual cla...

In this paper, we propose a new driver identification method using deep learning. Existing driver identification methods have the disadvantages that the size of the sliding time window is too large and the feature extraction is relatively subjective, which leads to low identification accuracy and long prediction time. We first propose using an unsu...

Cross-scene crowd counting plays a more and more important role in intelligent scene monitoring, and it is very important in the safety of personnel and the scene scheduling. The traditional estimation of crowd counting is mainly dependent on the simple background of scenes, which is not conducive to the complex background. To address this problem,...

Background and objective Freezing of gait (FOG) is a symptom that manifests as an episodic inability to move. It happens typically in patients with advanced Parkinson’s disease (PD), and it is a common cause of falls in PD patients. The management of FOG is extremely difficult due to its sudden and transient property. Methods In this study, we imp...

We propose a new feature representation algorithm using cross-covariance in the context of deep learning. Existing feature representation algorithms based on the sparse autoencoder and nonnegativity-constrained autoencoder tend to produce duplicative encoding and decoding receptive fields, which leads to feature redundancy and overfitting. We propo...