Due to the impact of weather forecasting on global human life, and to better reflect the current trend of weather changes, it is necessary to conduct research about the prediction of precipitation and provide timely and complete precipitation informa...
Conventionally, the maximum likelihood (ML) criterion is applied to train a deep belief network (DBN). We present a maximum entropy (ME) learning algorithm for DBNs, designed specifically to handle limited training data. Maximizing only the entropy o...
With success of Deep Belief Networks (DBNs) in computer vision, DBN has attracted great attention in hyperspectral classification. Many deep learning based algorithms have been focused on deep feature extraction for classification improvement. Multi-...
In some online social network services (SNSs), the members are allowed to label their relationships with others, and such relationships can be represented as the links with signed values (positive or negative). The networks containing such relations...
Virtual screening (VS) is a computational practice applied in drug discovery research. VS is popularly applied in a computer-based search for new lead molecules based on molecular similarity searching. In chemical databases similarity searching is us...
Aiming at the problems of poor efficiency of the intelligent fault diagnosis method of the main reducer and the poor effectiveness of multichannel data fusion, this paper proposes a multichannel data fusion method based on deep belief networks and ra...
In this research study, we investigate the ability of deep learning neural networks to provide a mapping between features of a parallel distributed discrete-event simulation (PDDES) system (software and hardware) to a time synchronization scheme to o...
This paper proposes an Adam-optimized Deep Belief Networks (Adam-DBNs) denoising method for throat-attached piezoelectric signals. The method aims to process mechanical vibration signals captured through polyvinylidene fluoride (PVDF) sensors attache...
Tire defects are crucial for safe driving. Specialized experts or expensive tools such as stereo depth cameras and depth gages are usually used to investigate these defects. In image processing, feature extraction, reduction, and classification are p...
Breast cancer is estimated to be the leading cancer type among new cases in American women. Core biopsy data have shown a close association between breast hyperplasia and breast cancer. The early diagnosis and treatment of breast hyperplasia are extr...
The adoption of cooperative intelligent transportation systems (cITSs) improves road safety and traffic efficiency. Vehicles connected to cITS form vehicular ad hoc networks (VANET) to exchange messages. Like other networks and systems, cITSs are tar...
In Geographical Information Systems, geo-coding is used for the task of mapping from implicitly geo-referenced data to explicitly geo-referenced coordinates. At present, an enormous amount of implicitly geo-referenced information is hidden in unstruc...
The rapid evolution of 5G and emerging 6G networks has increased system complexity, data volume, and security risks, making anomaly detection vital for ensuring reliability and resilience. However, existing machine learning (ML)-based approaches stil...
The applications of the deep belief network (DBN) for addressing practical engineering issues have recently emerged all over the world thanks to its accuracy and availability of data. In this paper, a predictive model using DBN was employed to invest...
To improve the accuracy of shallow neural networks in processing complex signals and cable fault diagnosis, and to overcome the shortage of manual dependency and cable fault feature extraction, a deep learning method is introduced, and a time−f...
Change detection from synthetic aperture radar (SAR) images is of great significance for natural environmental protection and human societal activity, which can be regarded as the process of assigning a class label (changed or unchanged) to each of t...
Dementias that develop in older people test the limits of modern medicine. As far as dementia in older people goes, Alzheimer’s disease (AD) is by far the most prevalent form. For over fifty years, medical and exclusion criteria were used to di...
Industrial robots have been increasingly used in the field of intelligent manufacturing. The low absolute positioning accuracy of industrial robots is one of the difficulties in their application. In this paper, an accuracy compensation algorithm for...
A cable-supported bridge is usually a key junction of a highway or a railway that demands a higher safety margin, especially when it is subjected to harsh environmental and complex loading conditions. In comparison to short-span girder bridges, long-...
Machine learning plays an important role in building intrusion detection systems. However, with the increase of data capacity and data dimension, the ability of shallow machine learning is becoming more limited. In this paper, we propose a fuzzy aggr...
The daily life-log routines of elderly individuals are susceptible to numerous complications in their physical healthcare patterns. Some of these complications can cause injuries, followed by extensive and expensive recovery stages. It is important t...
Due to the existing large-scale grid-connected photovoltaic (PV) power generation installations, accurate PV power forecasting is critical to the safe and economical operation of electric power systems. In this study, a hybrid short-term forecasting...
In this paper; we introduce an enhanced electromyography (EMG) pattern recognition algorithm based on a split-and-merge deep belief network (SM-DBN). Generally, it is difficult to classify the EMG features because the EMG signal has nonlinear and tim...
Effective and accurate water demand prediction is an important part of the optimal scheduling of a city water supply system. A novel deep architecture model called the continuous deep belief echo state network (CDBESN) is proposed in this study for t...
A deep belief network (DBN) is a powerful generative model based on unlabeled data. However, it is difficult to quickly determine the best network structure and gradient dispersion in traditional DBN. This paper proposes an improved deep belief netwo...
A non-destructive identification method was developed here based on dropout deep belief network in multi-spectral data of ancient ceramic. A fractional differential algorithm was proposed to enhance the spectral details by making use of the differenc...
Mechanical fault prediction is one of the main problems in condition-based maintenance, and its purpose is to predict the future working status of the machine based on the collected status information of the machine. However, on one hand, the model h...
As an important research direction of human–computer interaction technology, gesture recognition is the key to realizing sign language translation. To improve the accuracy of gesture recognition, a new gesture recognition method based on four c...
The electronic control module is an important part of a digital electronic detonator, which undergoes a complex production process that includes three electrical performance tests and three visual inspection procedures. In each inspection procedure,...
Mechanical equipment fault detection is critical in industrial applications. Based on vibration signal processing and analysis, the traditional fault diagnosis method relies on rich professional knowledge and artificial experience. Achieving accurate...
Incipient faults in power cables are a serious threat to power safety and are difficult to accurately identify. The traditional pattern recognition method based on feature extraction and feature selection has strong subjectivity. If the key feature i...
The prediction of cold load in ice-storage air conditioning systems plays a pivotal role in optimizing air conditioning operations, significantly contributing to the equilibrium of regional electricity supply and demand, mitigating power grid stress,...
Recently, the quality of fresh water resources is threatened by numerous pollutants. Prediction of water quality is an important tool for controlling and reducing water pollution. By employing superior big data processing ability of deep learning it...
The controlled interaction of work material and cutting tool is responsible for the precise outcome of machining activity. Any deviation in cutting parameters such as speed, feed, and depth of cut causes a disturbance to the machining. This leads to...
According to the complex fault mechanism of direct current (DC) charging points for electric vehicles (EVs) and the poor application effect of traditional fault diagnosis methods, a new kind of fault diagnosis method for DC charging points for EVs ba...
With the rapid development of information technology, the problem of the network security of unmanned aerial vehicles (UAVs) has become increasingly prominent. In order to solve the intrusion detection problem of massive, high-dimensional, and nonlin...
Based on the characteristics of remote sensing images of mine vegetation, this research studied the application of deep belief network model in mine vegetation identification. Through vegetation identification and classification, the ecological envir...
Deep learning has emerged as a new area of machine learning research. It is an approach that can learn features and hierarchical representation purely from data and has been successfully applied to several fields such as images, sounds, text and moti...
If an accident occurs during charging of an electric vehicle (EV), it will cause serious damage to the car, the person and the charging facility. Therefore, this paper proposes a fault warning method for an EV charging process based on an adaptive de...
The converter transformer is a special power transformer that connects the converter bridge to the AC system in the HVDC transmission system. Due to the special structure of the converter transformer, it is necessary to test its operation state durin...
To cope with the complex electromagnetic environment and varied signal styles, a novel method based on the energy cumulant of short time Fourier transform and reinforced deep belief network is proposed to gain a higher correct recognition rate for ra...
Deep belief networks (DBNs) have been widely applied in hyperspectral imagery (HSI) processing. However, the original DBN model fails to explore the prior knowledge of training samples which limits the discriminant capability of extracted features fo...
Thermoacoustic oscillation is indeed a phenomenon characterized by the symmetric coupling of thermal and acoustic waves. This paper introduces a novel approach for monitoring and predicting thermoacoustic combustion instability using a combination of...
Groundwater Contamination Source Identification (GCSI) is a crucial prerequisite for conducting comprehensive pollution risk assessments, formulating effective groundwater contamination control strategies, and devising remediation plans. In previous...
In load predication, point-based forecasting methods have been widely applied. However, uncertainties arising in load predication bring significant challenges for such methods. This therefore drives the development of new methods amongst which interv...
Water demand forecasting applies data supports for the scheduling and decision-making of urban water supply systems. In this study, a new dual-scale deep belief network (DSDBN) approach for daily urban water demand forecasting was proposed. Original...
Accurately predicting driving behavior can help to avoid potential improper maneuvers of human drivers, thus guaranteeing safe driving for intelligent vehicles. In this paper, we propose a novel deep belief network (DBN), called MSR-DBN, by integrati...
Sustainable urban development is a focus of regional policy makers; therefore, how to measure and understand urban growth is an important research topic. This paper quantified the amount of urban growth on land use maps that were derived from multi-t...
With a large number of distributed generators (DGs) and electrical vehicles (EVs) integrated into the power distribution system, the complexity of distribution system operation is increased, which arises to higher requirements for online reactive pow...
The spatial resolution of current soil moisture (SM) products is generally low, consequently limiting their applications. In this study, a deep belief network-based method (DBN) was used to downscale the Soil Moisture Active Passive (SMAP) L4 SM prod...
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