Research on routing optimisation of logistics distribution vehicle based on cloud model

To investigate the potential of conventional and deep learning techniques to recognize the species and distribution of mould in unhulled paddy, samples were inoculated and cultivated with five species of mould, and sample images were captured. The mould recognition methods were built using support vector machine (SVM), back-propagation neural network (BPNN), convolutional neural network (CNN), and deep belief network (DBN) models. An accuracy rate of 100% was achieved by using the DBN model to identify the mould species in the sample images based on selected colour-histogram parameters, followed by the SVM and BPNN models. A pitch segmentation recognition method combined with different classification models was developed to recognize the mould colony areas in the image. The accuracy rates of the SVM and CNN models for pitch classification were approximately 90% and were higher than those of the BPNN and DBN models. The CNN and DBN models showed quicker calculation speeds for recognizing all of the pitches segmented from a single sample image. Finally, an efficient uniform CNN pitch classification model for all five types of sample images was built. This work compares multiple classification models and provides feasible recognition methods for mouldy unhulled paddy recognition.

Surface roughness is a crucial factor affecting the surface quality of workpieces in manufacturing industries. Thus, it is important to provide an accurate performance of surface roughness prediction and optimal parameters to reduce the burden of time and costs during the process. In this study, two predict models, namely multiple linear regression and deep belief network (DBN) models, were performed to accurately predict change in surface roughness in the rotational electromagnetic finishing (REMF). Compared to the statistical-based model, the data-driven model based on the DBN architecture was a significantly considerable effect on surface roughness prediction in the REMF process. Among the considered DBN models, DBN5 architecture as [7, 14, 14, 1] showed effective features of the nonlinear relationship between process parameters and response with the highest determination coefficient (R2) of 0.9340 and the lowest mean squared error (MSE) of 1.3037 \(\times\) 10−3 in the testing datasets. In addition, a genetic algorithm (GA) as a heuristic optimization technique was adopted to optimize the input parameters of the best derived DBN model. It showed that the maximum change in surface roughness was 0.530 at particle length of 3 mm, particle diameter of 0.7 mm, particle weight of 1.3 kg, liquid water quantity of 1.0 l, a rotational speed of 1323 rpm, working time of 35 min, and initial surface roughness of 2.5478 m\(\upmu\).

The prediction of sleep quality using wearable-assisted smart health monitoring systems based on statistical data

Aiming at the problems of complex urban road network, low efficiency of logistics distribution, and the difficulty of large-scale logistics distribution area division and routing planning, this paper proposes a two-stage logistics distribution vehicle routing optimization (VRP) method based on the establishment of a multi-factor complex road network constrained logistics distribution mathematical model. Considering the complex traffic elements and road network topological structure in logistics and distribution, in the first stage, a heuristic simulated annealing (HSA) distribution region partitioning algorithm is proposed with the objective of balancing vehicle task load to divide the urban logistics distribution network under complex road networks, so as to reduce the area scale and path search cost. In the second stage of route decision making, aiming at minimizing the total cost of logistics distribution, combining the VRP problem with complex road network conditions, a heuristic path search method combined with complex road network model constraints is proposed. In this stage, a hybrid genetic beam search(HGBS) algorithm is used to plan the path nodes, reduce the randomness of the model in the initial search for paths by heuristic genetic algorithms, then combine with Beam graph search methods to reduce the space and time used for the search, and use optimization algorithms to improve the accuracy of independent sub-region routing optimization and the rationality of overall physical distribution route selection. Finally, the proposed method is validated in this paper with two practical cases. The experimental results show that the two-stage decision-making algorithm proposed in this paper has certain advantages in partitioning schemes, minimizing total cost and iteration times. Through comparison, the optimization ability of this method for logistics distribution networks is proved.

Experimental and DBN-Based neural network extraction of radiation attenuation coefficient of dry mixture shotcrete produced using different additives

The landslide susceptibility assessment is an essential part of landslide disaster risk identification and prevention. However, the binarization of the hidden layer limits the parameterization ability of the conditional probability of visible layer, making the training process of restricted Boltzmann machine more difficult and further limiting the accuracy and efficiency of deep belief network (DBN) model in landslide susceptibility assessment. Therefore, this study proposed a landslide susceptibility assessment method based on Auto-Encoder (AE)-modified DBN. Zhouqu County, Gansu Province in the People’s Republic of China, was selected as the study area. Historical landslides in Zhouqu County were identified using small baseline subset interferometric synthetic aperture radar technology and optical image. Landslide factors were screened based on a geographical detector and stepwise regression method. The Logcosh loss function and determinant coefficient R 2 index were used to evaluate the training process of the AE model, and the balanced cross entropy loss function was used to evaluate the entire network training process. In addition, the area under the curve (AUC) of the synthetical index model (SIM), support vector machine (SVM), and multilayer perceptron (MLP) were compared and evaluated. The results indicated that the proposed model could significantly improve the accuracy of landslide susceptibility assessment. The AUC value of the proposed model was 0.31, 0.12, and 0.11 higher than that of SIM, SVM, and MLP, respectively. Therefore, the improved DBN model based on AE proposed is reliable for early landslide identification and prediction.

Solar power is a type of renewable energy system that uses solar energy to produce electricity, and is regarded as one of the most important power sources in Taiwan. Since sunshine duration affects the amount of energy that can be generated by a solar power, the seasons of the year are important factors that should be considered for accurate solar power prediction. In the last decade, the use of artificial intelligence for forecasting systems have been quite popular, and the deep belief network (DBN) models started getting more attention. In this study, a seasonal deep belief network (SDBN) was developed to forecast monthly solar power output data. The SDBN was constructed by combining seasonal decomposition method and DBN. Further, this study used monthly solar power output data from the Taiwan Power Company. The results indicated that the proposed forecasting system demonstrated a superior performance in terms of forecasting accuracy. Also, the performance of autoregressive integrated moving average (ARIMA), generalized regression neural network (GRNN), and DBN obtained from a separate study were compared to the performance of the proposed SDBN model and showed that the latter was better than the other three models. Thus, the SDBN model can be used as an alternative method for monthly solar power output data forecasting.

With the rapid development of artificial intelligence technology of deep learning method, it has been applied to many fields, especially to life science. In this paper, a novel approach for the task of voiceprint recognition was proposed. The combination of deep belief network (DBN) and support vector machine (SVM) was used to identify the voiceprint of 10 different individuals. Based on a 24-dimension Mel Frequency Cepstrum Coefficient (MFCC), the authors extracted 256-dimension deep voiceprint features via DBN model that is developed by stacking three layers of Restricted Boltzmann Machine (RBM), and conducted voiceprint recognition by approach of SVM. According to the recognition results, the new approach can significantly improve both of accuracy and efficiency when it was compared with traditional voiceprint features and recognition models. The low dimensional features of voiceprint was extracted into higher dimensions by DBN model, while SVM can avoid the elevation of computation complexity caused by the increases of feature dimension. The combined strengths have been fully expressed by the experimental results.

Logistics distribution vehicle planning is an important issue in logistics transportation activities, and it is also a research hotspot in theoretical circles at home and abroad. At present, many studies have focused on establishing vehicle planning models and optimizing vehicle planning in different environments and have achieved rich results. As an important part of transportation production process, the efficiency of logistics distribution is very important to the whole production process. Especially for emergency logistics, every minute is very critical for emergency situations such as disaster relief. In order to improve the efficiency of emergency logistics, this paper applies multiagent technology to emergency logistics and puts forward an integrated modeling method of enterprise macromodeling, business process mesomodeling, and micromodel design. Using the agent-oriented system development method, an emergency logistics distribution vehicle planning model system is established. The development process of multiagent automatic trading system is described. The results show that it is feasible and effective to use multi-intelligent fuselage technology for emergency logistics distribution vehicle planning and decision-making. The algorithm proposed in this paper has advantages over the container order sequence processing scheme, and the total cost of order acceptance decreases sharply in the initial stage, which shows the practical convergence of the algorithm. The adjacency search method and Tabu search method deal with the calculation of total labor cost, and the Tabu neighborhood search algorithm obtains better results with lower labor cost.

A logistics distribution vehicle scheduling model under cloud computing environment is established based on the analysis of factors affecting resource scheduling. The order information and logistics distribution vehicle information processing are completed under the framework of cloud computing, so as to obtain the most reasonable logistics distribution plan. To solve the problem of vehicle allocation in logistics distribution, a distribution path algorithm model and a minimum delivery cost algorithm model are established to provide the best strategy for logistics distribution scheme.

Logistics distribution vehicle scheduling plays an important role in the supply chain. With the wide application of e-commerce technology and the increasing diversification of urban industrial and commercial development mode, the optimal scheduling of logistics distribution vehicles can improve the economic benefits of logistics and realize the scientization of logistics. Aiming at the problems existing in the logistics allocation system, this paper proposes a logistics allocation system model based on a heterogeneous neural network, uses the heterogeneous neural network to optimize vehicle scheduling, and gives the specific steps to solve the problem of optimal scheduling of distribution vehicles. The simulation consequences exhibit that the proposed technique cannot solely efficaciously clear up the automobile scheduling optimization model and however additionally has low pc complexity, excessive computational effectivity, and speedy convergence speed. The practicability and effectiveness of the improved algorithm are verified. When the number of distribution customers and distribution cycle is the same, the proposed algorithm effectively reduces the total distribution mileage, reduces the number of vehicles, and improves the efficiency of logistics distribution.

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 is possible to improve the accuracy of prediction. This paper proposes a method for predicting water quality based on the deep belief network (DBN) model. First, the particle swarm optimization (PSO) algorithm is used to optimize the network parameters of the deep belief network, which is to extract feature vectors of water quality time series data at multiple scales. Then, combined with the least squares support vector regression (LSSVR) machine which is taken as the top prediction layer of the model, a new water quality prediction model referred to as PSO-DBN-LSSVR is put forward. The developed model is valued in terms of the mean absolute error (MAE), the mean absolute percentage error (MAPE), the root mean square error (RMSE), and the coefficient of determination ( R 2 ). Results illustrate that the model proposed in this paper can accurately predict water quality parameters and better robustness of water quality parameters compared with the traditional back propagation (BP) neural network, LSSVR, the DBN neural network, and the DBN-LSSVR combined model.

Secure blockchain enabled Cyber–physical systems in healthcare using deep belief network with ResNet model

Although IoT sectors seem more popular and pervasively, they struggle with hazards. The botnet is one of the largest security dangers associated with IoT. It enables malicious software to administer and attack private network equipment collectively without the owners’ knowledge. Although many studies have used ML to detect botnets, these are either not very effective or only work with specific types of botnets or devices. As a result, the detection model for deep learning ideas is the focus of this research. It entails three key processes: (a) preprocessing, (b) feature extraction, and (c) classification. The input data are initially preprocessed using an improved data normalization approach. The preprocessed data are used to extract a number of features, including Tanimoto coefficient features, improved differential holoentropy-based features, Pearson r correlation-based features, and others. The detection process will be completed by an ensemble classification model that randomly shuffles models like the Deep Belief Network (DBN) model, Bidirectional Gated Recurrent Unit (Bi-GRU), and Long Short-Term Memory (LSTM). Bi-GRU, DBN, and LSTM will be averaged to provide the ensemble results. Bi-GRU is trained using the Self Improved Blue Monkey Optimization (SIBMO) Algorithm by selecting the optimal weights, which increases the detection accuracy. The overall performance of the suggested work is then evaluated in relation to other existing models using various methodologies. In comparison to existing methods, the created ensemble classifier [Formula: see text] SIBMO scheme obtains the highest accuracy (93%) at a learning percentage of 90%.

Automated biomedical signal processing becomes an essential process to determine the indicators of diseased states. At the same time, latest developments of artificial intelligence (AI) techniques have the ability to manage and analyzing massive amounts of biomedical datasets results in clinical decisions and real time applications. They can be employed for medical imaging; however, the 1D biomedical signal recognition process is still needing to be improved. Electrocardiogram (ECG) is one of the widely used 1-dimensional biomedical signals, which is used to diagnose cardiovascular diseases. Computer assisted diagnostic models find it difficult to automatically classify the 1D ECG signals owing to time-varying dynamics and diverse profiles of ECG signals. To resolve these issues, this study designs automated deep learning based 1D biomedical ECG signal recognition for cardiovascular disease diagnosis (DLECG-CVD) model. The DLECG-CVD model involves different stages of operations such as pre-processing, feature extraction, hyperparameter tuning, and classification. At the initial stage, data pre-processing takes place to convert the ECG report to valuable data and transform it into a compatible format for further processing. In addition, deep belief network (DBN) model is applied to derive a set of feature vectors. Besides, improved swallow swarm optimization (ISSO) algorithm is used for the hyperparameter tuning of the DBN model. Lastly, extreme gradient boosting (XGBoost) classifier is employed to allocate proper class labels to the test ECG signals. In order to verify the improved diagnostic performance of the DLECG-CVD model, a set of simulations is carried out on the benchmark PTB-XL dataset. A detailed comparative study highlighted the betterment of the DLECG-CVD model interms of accuracy, sensitivity, specificity, kappa, Mathew correlation coefficient, and Hamming loss.