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Precipitation inversion techniques are crucial in meteorological research, aiming to accurately detect the location and intensity of precipitation events, which is vital for extreme weather response strategies. This study introduces a new infrared precipitation inversion method, residual deep forest, which is based on a deep forest model with two key enhancements. First, a feature optimization filter module is used to ensure high correlation among features in the cascade forest, a component of the deep forest model. This optimization reduces computational burden while maintaining efficiency. Second, embedding a residual structure within the cascade forest creates the residual cascade forest, improving feature expression, information processing, and computational efficiency on high-dimensional, large-scale precipitation data. Experimental results show that residual deep forest surpasses traditional deep forest models and other classical machine learning techniques in precipitation inversion accuracy, especially in identifying high-intensity rainfall. The model achieves an average recall (AR) of 85.97%, a probability of detection of 75.46%, and a false alarm ratio of 49.13%. Ablation experiments demonstrate that the feature optimization filter module reduces training time by 3.56%, testing time by 1.24%, and memory usage by 3.97%, while the residual cascade forest module reduces these metrics by 1.30%, 0.90%, and 2.23%, respectively. The improvements in AR, probability of detection, and false alarm ratio, along with the reduction in computational resources, highlight the model's enhanced efficiency and performance. This method leverages infrared remote sensing to enhance the scope and accuracy of precipitation monitoring, reducing reliance on radar and other ground-based instruments, and significantly improving precipitation inversion accuracy while minimizing spatial and temporal complexity.

Abstract Intelligent traffic light control (ITLC) algorithms are very efficient for relieving traffic congestion. Recently, many decentralized multi-agent traffic light control algorithms are proposed. These researches mainly focus on improving reinforcement learning method and coordination method. But, as all the agents need to communicate while coordinating with each other, the communication details should be improved as well. To guarantee communication effectiveness, two aspect should be considered. Firstly, a traffic condition description method need to be designed. By using this method, traffic condition can be described simply and clearly. Secondly, synchronization should be considered. As different intersections have different cycle lengths and message sending event happens at the end of each traffic signal cycle, every agent will receive messages of other agents at different time. So it is hard for an agent to decide which message is the latest one and the most valuable. Apart from communication details, reinforcement learning algorithm used for traffic signal timing should also be improved. In the traditional reinforcement learning based ITLC algorithms, either queue length of congested cars or waiting time of these cars is considered while calculating reward value. But, both of them are very important. So a new reward calculation method is needed. To solve all these problems, in this paper, a new ITLC algorithm is proposed. To improve communication efficiency, this algorithm adopts a new message sending and processing method. Besides, to measure traffic congestion in a more reasonable way, a new reward calculation method is proposed and used. This method takes both waiting time and queue length into consideration.

Characterized by decentration, anonymity, strong robustness and tamper resistance, blockchain accompanied by digital currency is a natural carrier for building the covert communication channel.First, the definition and development history of the network covert channel, the framework of blockchain technology and the traditional network covert channel were introduced and the advantages of the channel in the blockchain were given.Thus, the channel in blockchain was classified on the basis of transaction address, signature algorithm, smart contract and P2P broadcast mechanism.The merits and demerits were analyzed in terms of crypticity, transmission efficiency, and communication costs.Lastly, problems and the future research direction of the network covert channel in blockchain were presented.

The core challenge of speech synthesis technology is how to convert text information into an audible audio form to meet the needs of users. In recent years, the quality of speech synthesis based on end-to-end speech synthesis models has been significantly improved. However, due to the characteristics of the Mongolian language and the lack of an audio corpus, the Mongolian speech synthesis model has achieved few results, and there are still some problems with the performance and synthesis quality. First, the phoneme information of Mongolian was further improved and a Bang-based pre-training model was constructed to reduce the error rate of Mongolian phonetic synthesized words. Second, a Mongolian speech synthesis model based on Ghost and ILPCnet was proposed, named the Ghost-ILPCnet model, which was improved based on the Para-WaveNet acoustic model, replacing ordinary convolution blocks with stacked Ghost modules to generate Mongolian acoustic features in parallel and improve the speed of speech generation. At the same time, the improved vocoder ILPCnet had a high synthesis quality and low complexity compared to other vocoders. Finally, a large number of data experiments were conducted on the proposed model to verify its effectiveness. The experimental results show that the Ghost-ILPCnet model has a simple structure, fewer model generation parameters, fewer hardware requirements, and can be trained in parallel. The average subjective opinion score of its synthesized speech reached 4.48 and the real-time rate reached 0.0041. It ensures the naturalness and clarity of synthesized speech, speeds up the synthesis speed, and effectively improves the performance of the Mongolian speech synthesis model.

Raised-floor data centers usually suffer from the local hotspots resulted from uneven cool air delivery. These hotspots not only degrade server performance, but also threat equipment reliability. The commonly used industrial practice of increasing the Computer Room Air Conditioner (CRAC) blower speed for removing hotspots is energy inefficient and may lead to overcooling of some servers. In this paper, we explore the potential of active tiles in data center cooling management. In particular, we deploy a prototype of active tile in a production data center and conduct extensive experiments to investigate the cooling performance. It is shown that deploying the active tiles with even 10% fan speed increases the tile flow by 49%, and sealing the under-rack gap reduces the rack bottom temperature by up to 6°C. Moreover, three machine learning techniques, i.e., Gaussian Process Regression (GPR), Artificial Neural Network (ANN), and Multivariate Linear Regression (MLR) are employed to construct end-to-end data-driven thermal models for the active tile. Using field measured data as training and testing data sets, it is concluded that GPR and ANN are competent for accurate thermal modeling of active tiles. Specifically, GPR achieves the smallest prediction error which is around 0.3°C.

Abstract The main purpose of this paper is to improve the resilience of data center cooling system and reduce the energy consumption in data center by using the Tilted Active Tile (TATs). We deploy multiple TATs in cold aisle environment then analyze the thermal performance of racks. TATs also help to improve the reliability in case of cooling failure by increased ride through time. We observed an extended ride‐through time for data centers deployed with Tilted Active Tiles (TATs) when the CRAC blower failed. Proposed approach can maintain cooling system temperature for up to 10 minutes. We also introduced ANN based thermal models to predict the thermal performance of TATs, To train the ANN models, we collect the rack inlet and CRAC outlet temperature from Data Center. We also fixed blank panel in empty U part of racks and then analyzed that the thermal performance of racks are better and temperature distribution at bottom, middle and top of the rack were same under 30°angle. The airflow of TATs and passive tile influence on surrounding racks. That’s why we analyzed the disturbance of TATs on surrounding rack and observed that the TATs at 30°angle has positiveimpact on the surrounding racks.

With the fast development of network technology, great amount of data have been accumulated. Plenty of them are organized by using heterogeneous information networks (HIN). So mining heterogeneous information networks efficiently is very important. Node clustering is an essential part of this task. And several clustering algorithms have been proposed. As all these algorithms contain complicated optimization procedure and matrix calculation procedure, complexity of these algorithms is very high. To overcome the shortage described above, in this paper, a new clustering algorithm is proposed. In this algorithm, several parameters should be inputted. These parameters include a heterogeneous information network, meta-paths that are used and the names of target types. During the clustering procedure, a homogeneous network will be built by the proposed algorithm firstly. All the target objects of HIN are treated as nodes of this network. The instances of meta-paths are edges. After the homogeneous network is constructed, label propagation procedure can be performed. Then the clustering result will be obtained. Obviously, by using the proposed algorithm to perform clustering, the complex optimization procedure and matrix calculation procedure are eliminated. As the convergence rate of label propagation procedure is fast, the proposed algorithm is very efficient. Besides, we can find that label propagation procedure can be executed in parallel. Thus, the proposed algorithm is easy to be parallelized. In this situation, it is fit for processing large scale HIN based on server cluster. From experimental results, we can find that the proposed algorithm running faster than all the other algorithms for comparison.

Sequence alignment is a key link of bioinformatics analysis. The basic local alignment search tool (BLAST) is a popular sequence alignment algorithm with high accuracy. However, the BLAST is inefficient in comparing and analyzing a massive amount of gene sequencing data. To solve the problem, this paper designs a distributed parallel BLAST method called SparkBLAST, based on the big data technique Spark. Under the in-memory computing framework Spark, SparkBLAST identifies the task of sequence alignment, divides the sequence dataset, and compares the sequence data. The Apache Hadoop YARN was adopted to task scheduling and resource allocation. Finally, the SparkBLAST was compared with standalone BLAST through experiments. The results show that SparkBLAST realized the speedup ratio of 3.95, without sacrificing the accuracy. In other words, SparkBLAST greatly outshines the standalone BLAST in calculation efficiency. The research findings provide bioinformatics researchers a highly efficient tool for sequence alignment.

A framework for traffic congestion prediction and visualization based on machine learning and Fuzzy Comprehensive Evaluation named MF-TCPV is proposed in this paper. The framework uses DataX and DataV to implement the integration of multi-source heterogeneous traffic data and the visualization of congestion prediction results. A deep prediction model named LSTM-SPRVM based on deep learning algorithms, machine learning algorithms, and Spark parallelization technology for the prediction of traffic congestion features in the future is proposed. In MF-TCPV, traffic congestion is divided into six levels based on Fuzzy Comprehensive Evaluation and traffic congestion features such as average speed, road occupancy rate, and traffic flow density. MF-TCPV is validated based on the real data of Whitemud Drive in Canada. The experimental results demonstrate that MF-TCPV is capable of predicting the traffic congestion accurately and displaying prediction results visually. LSTM-SPRVM is better than other existing deep learning models in terms of prediction accuracy, and MF-TCPV can intuitively visualize the prediction results of traffic congestion.

With the fast development of Internet, many fields have accumulated great amount of data. And plenty of them are organized in heterogeneous information network (HIN), so analyzing HIN efficiently is very necessary. Several HIN clustering algorithms have been proposed in recent years. Most of them are based on meta-path. As instances of meta-paths can connect all the target objects directly, while clustering, all these algorithms only focus on the relationship of two target objects that are connected directly by an instance of meta-paths. In this situation, information contained in the relationship of two target objects that are not directly connected by an instance of meta-paths is neglected. These target object pairs may be very helpful for obtaining better clustering result. In order to take these target object pairs into consideration, a structural neighbor searching method is applied into the proposed algorithm. By using this method, all the indirectly connected neighbors of target objects are considered while performing clustering. Moreover, as more than one meta-paths can be found in a heterogeneous information network and each meta-path can impact the clustering result in different degree, a weight value should be assigned to each meta-path. These weight values are used to represent the relative importance of meta-paths. To obtain these weight values, a calculation method is proposed and the algorithm described in this paper tries to calculate weight values by using this method. With weight values of meta-paths and structural neighbors of target objects, all the vectors of target objects can be calculated. Then the hierarchical clustering procedure will be performed based on these vectors.

This paper proposes the design and the implementation of a Spark parallelization plan for improving the Smith-Waterman (SW) algorithm, named the Spark-OSW algorithm. Then, the Spark-OSW was verified through accuracy, performance and acceleration tests. The results show that the proposed algorithm achieved 100% accuracy, ran much faster than the SW, and performed well in cluster environment. The research findings shed important new light on the database search for gene sequences.

Nowadays, number of private cars is increasing rapidly. Traffic congestion becomes a serious problem in urban region. If traffic congestion can be predicted before it happens, it will be helpful for improving traffic condition. So many traffic congestion prediction methods have been proposed. Almost all these methods are based on traffic flow prediction algorithm. In these methods, historical traffic flow data is used while performing prediction. Obviously, information of sudden accidents like traffic accidents, road damage and bad weather that happened recently may be not contained in historical traffic flow data. But performance of traffic flow prediction algorithms will be affected by these factors. In this situation, performance of traffic congestion prediction method based on traffic flow prediction result will be affected as well. To solve the problem, a new traffic congestion prediction method based on trajectory mining algorithm is proposed in this paper. In this method, traffic controllers can set a threshold for each road according to the current situation of the road. The threshold represents the vehicle number that can be carried by the corresponding road in a short period. Besides, for each road, the proposed method tries to count the number of vehicles that will pass through the specific road at next time step by predicting next location for all the running vehicles based on their trajectories. If the vehicle number of a road surpasses the threshold of this road, it will be predicted as congested road. Otherwise, it will be predicted as non-congested road. [ABSTRACT FROM AUTHOR]