Sliding Window Control Research Articles

Sliding Window-Based High Utility Item-Sets Mining Over Data Stream Using Extended Global Utility Item-Sets Tree

High utility item-sets mining (HUIM) is a special topic in frequent item-sets mining (FIM). It gives better insights for business growth by focusing on the utility of items in a transaction. HUIM is evolving as a powerful research area due to its vast applications in many fields. Data stream processing, meanwhile, is an interesting and challenging problem since, processing very fast generating a huge amount of data with limited resources strongly demands high-performance algorithms. This paper presents an innovative idea to extract the high utility item-sets (HUIs) from the dynamic data stream by applying sliding window control. Even though certain algorithms exist to solve the same problem, they allow redundant processing or reprocessing of data. To overcome this, the proposed algorithm used a tree like structure called extended global utility item-sets tree (EGUI-tree), which is flexible to store and retrieve the mined information instead of reprocessing. An experimental study on real-world datasets proved that EGUI-tree algorithm is faster than the state-of-the-art algorithms.

Real-Time Fault Diagnosis for EVs With Multilabel Feature Selection and Sliding Window Control

Real-time fault diagnosis on vehicles can effectively avoid potential accidents, which, however, is difficult and challenging to be widely deployed due to the low computational capability and limited data storage of electric vehicles (EVs). To address this issue, we propose a vehicle-mounted fault diagnosis system with low computational complexity and small data storage, for achieving real-time monitoring of vehicle status. To facilitate the accurate and optimized feature selection, we had been collecting 6.52-GB real data from three EVs in 12 months. Motivated by those data, we first propose a multilabel feature selection algorithm to obtain the feature weights, based on which the optimal number of features is then calculated through the backpropagation neural network (BPNN), thus minimizing the computational cost of real-time fault diagnosis regarding sample dimensions. To further simplify the fault diagnosis system, i.e., reducing the minimum required capacity of data storage, we design a real-time diagnosis sliding window (RDSW) where the window moves forward as new samples arrive and the stale data outside the window are discarded. In particular, we calculate the optimal size of RDSW, which controls the minimum required number of samples to guarantee the accuracy of real-time fault diagnosis. Owing to the mechanism of RDSW, vehicles no longer need to store massive data to guarantee the accuracy of real-time fault diagnosis. In addition, the results of real-time fault diagnosis at each vehicle can be shared with other vehicles in cooperative intelligent transportation systems (C-ITS). Finally, comprehensive simulation is conducted to validate the effectiveness of the proposed diagnosis system in terms of accuracy, complexity and storage capacity.

Adaptive control algorithm with a retraining technique to predict the optimal amount of chilled water in a data center cooling system

We developed control algorithms based on one of three artificial-intelligence-based retraining techniques (sliding window, vector adaptation, and vector augmentation) to provide the optimal indoor temperature and save on the energy expenditure for cooling in data centers. The artificial neural network prediction model predicts the computer room air handler supply air temperature of a central chilled water system and is added to the control algorithm. The proposed algorithm can determine the optimal chilled water flow rate required to cool the server to the set temperature by using the predicted computer room air handler supply air temperature. We developed a control algorithm embedded in an artificial neural network predictive model that includes three retraining techniques. Afterward, we compared the control performance and verified its adaptability by using computer simulation. When using the algorithm with sliding window control, the root mean-squared error between the set temperature and the control temperature was 0.08 °C, the maximum error was 0.81 °C, and the cooling load was 21,026.27 kWh. The accuracy, stability, and energy-saving ability of the sliding window control algorithm were higher those of the other two algorithms, and its superior adaptability and scalability under changing environmental conditions were demonstrated.

Model and simulations of multipath bridge routing for inter-swarm UAV communications in EMANE/CORE

ABSTRACT The unmanned aerial vehicle (UAV) network is a special type of mobile ad hoc network (MANET). 1 There are two types of UAV swarm communications: Communications within a swarm (i.e. intra-swarm) and communication between different swarms (i.e. inter-swarm). This paper proposes the concept of bridge routing to establish the route(s) for efficient inter-swarm communications. The bridge routing scheme is a progressive process with two functions: UAV formation control and data transmissions. Based on the bridge routing topology, we have implemented the multipath user datagram protocol (MP-UDP), as a sub-layer between the transport and application layers. Compared with the multipath transmission control protocol (MP-TCP), MP-UDP is more suitable to the dynamic network topologies, since it does not use the high-overhead sliding window control. We have used the emulator tools, the Extendable Mobile Ad -hoc Network Emulator (EMANE) and Common Open Research Emulator (CORE) to implement and study the QoS performance of MP-UDP and bridge routing in UAV swarm networks. Our results demonstrate the improved throughput performance for inter-swarm communications. Approved for Public Release; distribution unlimited 88ABW-2020-2122 on 13 July 2020. Other requests shall be referred to AFRL/RIT 525 Brooks Rd Rome, NY 13,441

High Utility Sequential Pattern Mining over Data Streams with Sliding Window Control

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Ensemble Classification of Data Streams Based on Attribute Reduction and a Sliding Window

With the current increasing volume and dimensionality of data, traditional data classification algorithms are unable to satisfy the demands of practical classification applications of data streams. To deal with noise and concept drift in data streams, we propose an ensemble classification algorithm based on attribute reduction and a sliding window in this paper. Using mutual information, an approximate attribute reduction algorithm based on rough sets is used to reduce data dimensionality and increase the diversity of reduced results in the algorithm. A double-threshold concept drift detection method and a three-stage sliding window control strategy are introduced to improve the performance of the algorithm when dealing with both noise and concept drift. The classification precision is further improved by updating the base classifiers and their nonlinear weights. Experiments on synthetic datasets and actual datasets demonstrate the performance of the algorithm in terms of classification precision, memory use, and time efficiency.

Efficient High Utility Pattern Mining for Establishing Manufacturing Plans With Sliding Window Control

In industrial areas, understanding the preference of customers is one of the important considerations for establishing profitable product manufacturing plans. As one of the approaches in pattern mining, high utility pattern mining has been employed to find a set of products creating high profits by considering the purchase quantity and price of each product. In this regard, high utility pattern mining can be useful to establish profitable product manufacturing plans that allow a corporation to maximize its revenue. For establishing manufacturing plans, we also need to understand the recent preference of customers from stream data, which are continually generated without limitations. In this paper, we propose a novel algorithm and list structure for finding high utility patterns over data streams on the basis of a sliding window mode. Unlike existing algorithms, the proposed algorithm does not consume huge computational resources for verifying candidate patterns because it can avoid the generation of candidate patterns. Therefore, the algorithm efficiently works in complex dynamic systems. Experimental results obtained from various tests using real-world dataset show that the proposed algorithm outperforms state-of-the-art methods in terms of runtime, memory usage, and scalability.

Constraints of nonresponding flows based on cross layers in the networks

In the active queue management (AQM) scheme, core routers cannot manage and constrain user datagram protocol (UDP) data flows by the sliding window control mechanism in the transport layer due to the nonresponsive nature of such traffic flows. However, the UDP traffics occupy a large part of the network service nowadays which brings a great challenge to the stability of the more and more complex networks. To solve the uncontrollable problem, this paper proposes a cross layers random early detection (CLRED) scheme, which can control the nonresponding UDP-like flows rate effectively when congestion occurs in the access point (AP). The CLRED makes use of the MAC frame acknowledgement (ACK) transmitting congestion information to the sources nodes and utilizes the back-off windows of the MAC layer throttling data rate. Consequently, the UDP-like flows data rate can be restrained timely by the sources nodes in order to alleviate congestion in the complex networks. The proposed CLRED can constrain the nonresponsive flows availably and make the communication expedite, so that the network can sustain stable. The simulation results of network simulator-2 (NS2) verify the proposed CLRED scheme.

Optimal scheduling model for hub airport taxi based on improved ant colony collaborative algorithm

Taxiway connects parking spot and runway,which takes an important part in airport surface scheduling.According to the constraints of aircraft taxi conflict and dynamic allocation of runway's resources,improved ant colony collaborative algorithm and sliding window control were adopted to optimize taxi scheduling.Ensuring taxi conflict free and taking account of single flight taxi time,total taxi time of inbound and outbound flights was reduced.A domestic hub airport taxi scheduling simulation shows that the proposed method and model have obvious advantages,which can provide decision support for hub airport's taxi scheduling.