Dioxin (DXN), a by-product from the municipal solid waste incineration (MSWI) process, is an organic pollutant; thus, it is extremely harmful to the ecological environment and difficult to detect in ...real time. A selective ensemble (SEN) model for DXN emission concentration based on Bayesian inference and binary trees is proposed given the weak interpretability, high model complexity, and poor generalization performance of the existing DXN emission concentration prediction model. Initially, bagging sampling is used to obtain different data subsets. The binary tree, as the candidate submodel, is constructed based on the sub-datasets, and the prior information of the leaf nodes and the predicted values of the candidate submodel are calculated. Bayesian inference is used to calculate the posterior information to characterize the fitness of the candidate submodel. Based on the posterior error, the best submodel is selected as the ensemble submodel. These processes are repeated to obtain all the ensembled submodels and the corresponding posterior information. Then, the combined weights are determined by the posterior information of all ensemble submodels, and the DXN emission concentration SEN model is constructed. The effectiveness of the proposed method is verified using the actual data of the MSWI process.
•Binary Tree is integrated with Support Vector Data Description to address multi-classification issues with unbalanced datasets.•Separability measure based on Mahalanobis distance is proposed to ...construct Binary Tree.•The parameters of Support Vector Data Description are optimized using Particle Swarm Optimization to eliminate the error caused by manually selection.
In machinery fault diagnosis area, the obtained data samples under faulty conditions are usually far less than those under normal condition, resulting in unbalanced dataset issue. The commonly used machine learning techniques including Neural Network, Support Vector Machine, and Fuzzy C-Means, etc. are subject to high misclassification with unbalanced datasets. On the other hand, Support Vector Data Description is suitable for unbalanced datasets, but it is limited for only two class classification. To address the aforementioned issues, Support Vector Data Description based machine learning model is formulated with Binary Tree for multi-classification problems (e.g. multi fault classification or fault severity recognition, etc.) in machinery fault diagnosis. The binary tree structure of multiple clusters is firstly drawn based on the order of cluster-to-cluster distances calculated by Mahalanobis distance. Support Vector Data Description model is then applied to Binary Tree structure from top to bottom for classification. The parameters of Support Vector Data Description are optimized by Particle Swarm Optimization algorithm taking the recognition accuracy as objective function. The effectiveness of presented method is validated in the rotor unbalance severity classification, and the presented method yields higher classification accuracy comparing with conventional models.
The explosive generation of Internet of Things (IoT) data calls for cloud service providers (CSP) to further provide more secure and reliable transmission, storage, and management services. This ...requirement, however, goes against the honest and curious nature of CSP, to the extent that existing methods introduce the third-party audit (TPA) to check data security in the cloud. TPA solves the problem of unreliable CSP but puts a heavy burden on lightweight users because of the sheer amount of the pre-audit data processing work. In this paper, we establish an audit model based on a designed binary tree assisted by edge computing, which provides computing capability for the resource-constrained terminals. The data pre-processing task is offloaded to the edge, which reduces computing load and improves the efficiency of task processing. We propose an improved correlation mechanism between data blocks and nodes on the binary tree so that all nodes on the binary tree can be fully utilized while existing methods use only leaf nodes and thus are required to establish multiple binary trees. Moreover, to improve audit efficiency, the binary tree in the audit process is designed to be self-balanced. In experiments, we compare our methods with the traditional method and experimental results show that the proposed mechanism is more effective to store and manage big data, which is conducive to providing users with more secure IoT services.
In this paper we propose a new representation for FFT algorithms called the triangular matrix representation. This representation is more general than the binary tree representation and, therefore, ...it introduces new FFT algorithms that were not discovered before. Furthermore, the new representation has the advantage that it is simple and easy to understand, as each FFT algorithm only consists of a triangular matrix. Besides, the new representation allows for obtaining the exact twiddle factor values in the FFT flow graph easily. This facilitates the design of FFT hardware architectures. As a result, the triangular matrix representation is an excellent alternative to represent FFT algorithms and it opens new possibilities in the exploration and understanding of the FFT.
With rapid development of the global economy, economic growth is increasingly dependent on energy consumption, and the world faces the associated problems of environmental pollution and depletion of ...energy sources. This creates a challenge: Our ability to achieve these goals is severely constrained by the current governance based on non-cooperative energy conservation. To improve energy conservation in China, we developed an energy-conservation model based on market mechanisms. The model has three parts: (1) a two-objective (GDP and social benefits) optimization model; (2) a model that determines the optimal trading volume for energy conservation quotas in each province, including division of provinces into quota buyers and sellers and a cooperative game model for energy conservation quotas; and (3) a Nash distribution model for inter-provincial cooperation to fairly distribute the benefits from cooperation. We then used Shandong, Zhejiang, and Jiangsu provinces for an empirical analysis of the cooperative model. With the current territorial management approach, the social benefits of the inter-provincial cooperation based on option trading increased by 2.79 %, and GDP increased by 273.636 × 109 CNY. After a reasonable distribution of the benefits, each province benefited from the cooperation. This demonstrates that our model can improve China's current energy conservation governance.
•We developed a cooperative inter-provincial energy conservation model based on market mechanisms,and it considers both GDP and social benefits.•It combines option trading and it uses the binomial-tree option pricing model and derived a spot price derivative for energy conservation quotas.•It uses the Nash allocation model with cooperative game to fairly distribute the benefits among the cooperating provinces.
Block Partitioning Structure in the VVC Standard Huang, Yu-Wen; An, Jicheng; Huang, Han ...
IEEE transactions on circuits and systems for video technology,
10/2021, Volume:
31, Issue:
10
Journal Article
Peer reviewed
Open access
Versatile Video Coding (VVC) is the latest video coding standard jointly developed by ITU-T VCEG and ISO/IEC MPEG. In this paper, technical details and experimental results for the VVC block ...partitioning structure are provided. Among all the new technical aspects of VVC, the block partitioning structure is identified as one of the most substantial changes relative to the previous video coding standards and provides the most significant coding gains. The new partitioning structure is designed using a more flexible scheme. Each coding tree unit (CTU) is either treated as one coding unit or split into multiple coding units by one or more recursive quaternary tree partitions followed by one or more recursive multi-type tree splits. The latter can be horizontal binary tree split, vertical binary tree split, horizontal ternary tree split, or vertical ternary tree split. A CTU dual tree for intra-coded slices is described on top of the new block partitioning structure, allowing separate coding trees for luma and chroma. Also, a new way of handling picture boundaries is presented. Additionally, to reduce hardware decoder complexity, virtual pipeline data unit constraints are introduced, which forbid certain multi-type tree splits. Finally, a local dual tree is described, which reduces the number of small chroma intra blocks.
In the exploration of the next video coding standard, the quadtree plus binary tree block structure (QTBT) has been adopted in the joint video exploration team. QTBT splits a block into sub-blocks ...following a structure of quadtree or binary tree. However, all the split modes of QTBT are symmetrical, which is less efficient in coding asymmetrical motion. In addition, the lower signaling efficiency of side information occurs when a successive binary split in the same direction is selected. Motivated by these observations, the derived mode (DM) based on QTBT is proposed in this letter, where six novel DMs are introduced. The DM further splits a QTBT node into several sub-blocks, and each sub-block can be predicted independently. Among the DMs, four asymmetrical split modes are introduced to improve the capability of coding asymmetrical motion, and two 4-split modes are designed to improve signaling efficiency. Moreover, higher efficiency than QTBT, a 0.99% BD-rate saving on average, is verified by extensive experiments.
This paper proposes a local model network (LMN) for measurement-based modeling of the nonlinear aggregate power system loads. The proposed LMN approach requires no pre-defined standard load model and ...uses measurement data to identify load dynamics. Furthermore, due to the interesting characteristics of the proposed approach, the LMN is able to have separate and independent linear and nonlinear inputs, determined by the use of prior knowledge. Trained by the newly developed hierarchical binary tree (HBT) learning algorithm, the proposed LMN attains maximum generalizability with the best linear or nonlinear structure. The previous values of the power system voltage and active and reactive powers are considered as the inputs of the LMN. The proposed approach is applied to the artificially generated data and IEEE 39-bus test system. Work on the field measurement real data is also provided to verify the method. The results of modeling for artificial data, the test system and real data confirm the ability of the proposed approach in capturing the dynamics of the power system loads.
As resolution of on-board imaging spectrometer keeps improving, data acquisition rate increases and resource limited satellite environment necessitates for computationally simple data compression ...methods to meet timing, bandwidth and resource requirements with error resilience. This letter proposes a new lossless, prediction based algorithm for on-board satellite hyperspectral data compression that utilizes spectral as well as spatial correlation and at the same time, is computationally less complex. Concept of non-binary tree traversal is used with nearest neighbor method and implemented using neighbor driven decision making in pre-processing stage. Previously processed pixels are used to minimize the prediction residual, which makes more than 80% calculations causal in nature and thereby reducing the computational complexity of the algorithm. The prediction residual is then encoded using sample adaptive Golomb coding in band-sequential order. CCSDS corpus of data for hyperspectral images is used for evaluating the performance of the algorithm. The proposed method shows reduced computational complexity and lesser data dependencies compared to the CCSDS 123.0-B-1 standard when similar spectral vicinity is considered, and comparable compression performance compared to other state-of-the-art on-board lossless compression methods.
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