Implementation of decision trees for embedded systems

This research work develops real-time incremental learning decision tree solutions suitable for real-time embedded systems by virtue of having both a defined memory requirement and an upper bound on the computation time per training vector. In addition, the work provides embedded systems with the capabilities of rapid processing and training of streamed data problems, and adopts electronic hardware solutions to improve the performance of the developed algorithm. Two novel decision tree approaches, namely the Multi-Dimensional Frequency Table (MDFT) and the Hashed Frequency Table Decision Tree (HFTDT) represent the core of this research work. Both methods successfully incorporate a frequency table technique to produce a complete decision tree. The MDFT and HFTDT learning methods were designed with the ability to generate application specific code for both training and classification purposes according to the requirements of the targeted application. The MDFT allows the memory architecture to be specified statically before learning takes place within a deterministic execution time. The HFTDT method is a development of the MDFT where a reduction in the memory requirements is achieved within a deterministic execution time. The HFTDT achieved low memory usage when compared to existing decision tree methods and hardware acceleration improved the performance by up to 10 times in terms of the execution time

Co-optimisation of Planning and Operation forActive Distribution Grids

Given the increased penetration of smart grid technologies, distribution system operators are obliged to consider in their planning stage both the increased uncertainty introduced by non-dispatchable distributed energy resources, as well as the operational flexibility provided by new real-time control schemes. First, in this paper, a planning procedure is proposed which considers both traditional expansion measures, e.g. upgrade of transformers, cables, etc., as well as real-time schemes, such as active and reactive power control of distributed generators, use of battery energy storage systems and flexible loads. At the core of the proposed decision making process lies a tractable iterative AC optimal power flow method. Second, to avoid the need for a real-time centralised coordination scheme (and the associated communication requirements), a local control scheme for the operation of individual distributed energy resources and flexible loads is extracted from offline optimal power flow computations. The performance of the two methods is demonstrated on a radial, low-voltage grid, and compared to a standard local control scheme

Real-time chaotic video encryption based on multithreaded parallel confusion and diffusion

Due to the strong correlation between adjacent pixels, most image encryption schemes perform multiple rounds of confusion and diffusion to protect the image against attacks. Such operations, however, are time-consuming, cannot meet the real-time requirements of video encryption. Existing works, therefore, realize video encryption by simplifying the encryption process or encrypting specific parts of video frames, which results in lower security compared to image encryption. To solve the problem, this paper proposes a real-time chaotic video encryption strategy based on multithreaded parallel confusion and diffusion. It takes a video as the input, splits the frame into subframes, creates a set of threads to simultaneously perform five rounds of confusion and diffusion operations on corresponding subframes, and efficiently outputs the encrypted frames. The encryption speed evaluation shows that our method significantly improves the confusion and diffusion speed, realizes real-time 480x480, 576x576, and 768x768 24FPS video encryption using Intel Core i5-1135G7, Intel Core i7-8700, and Intel Xeon Gold 6226R, respectively. The statistical and security analysis prove that the deployed cryptosystems have outstanding statistical properties, can resist attacks, channel noise, and data loss. Compared with existing works, to the best of our knowledge, the proposed strategy achieves the fastest encryption speed, and realizes the first real-time chaotic video encryption that reaches the security level of image encryption. In addition, it is suitable for many confusion, diffusion algorithms and can be easily deployed with both hardware and software.Comment: 14 pages, 11 figures, 9 table

An IoT realization in an interdepartmental real time simulation lab for distribution system control and management studies

Modern electric distribution systems with emerging operation methods and advanced metering systems bring new challenges to the system analysis, control and management. Interdependency of cyber and physical layers and interoperability of various control and management strategies require wide and accurate test and analysis before field implementation. Real-time simulation is known as a precise and reliable method to support new system/device development from initial design to implementation. However, for the study of different application algorithms, considering the various expertise requirements, the interconnection of multiple development laboratories to a real-time simulation lab, which constitutes the core of an interdepartmental real-time simulation platform, is needed. This paper presents the implemented architecture of such an integrated lab, which serves real-time simulations to different application fields within electric distribution system domain. The architecture is an implementation of an Internet-of-Things to facilitate software in-the-loop (SIL) and hardware in-the-loop (HIL) tests. A demo of the proposed architecture is presented, applied to the testing of a fault location algorithm in a portion of a realistic distribution system model. The implemented platform is flexible to integrate different algorithms in a plug-and-play fashion through a designed communication interface

Hierarchical Time-Optimal Planning for Multi-Vehicle Racing

This paper presents a hierarchical planning algorithm for racing with multiple opponents. The two-stage approach consists of a high-level behavioral planning step and a low-level optimization step. By combining discrete and continuous planning methods, our algorithm encourages global time optimality without being limited by coarse discretization. In the behavioral planning step, the fastest behavior is determined with a low-resolution spatio-temporal visibility graph. Based on the selected behavior, we calculate maneuver envelopes that are subsequently applied as constraints in a time-optimal control problem. The performance of our method is comparable to a parallel approach that selects the fastest trajectory from multiple optimizations with different behavior classes. However, our algorithm can be executed on a single core. This significantly reduces computational requirements, especially when multiple opponents are involved. Therefore, the proposed method is an efficient and practical solution for real-time multi-vehicle racing scenarios.Comment: 6 pages, accepted to be published as part of the 26th IEEE International Conference on Intelligent Transportation Systems (ITSC 2023), Bilbao, Bizkaia, Spain, September 24-28, 202

Scheduling Issues in Real-Time Systems

The most important objective of real-time systems is to fulfill time-critical missions in satisfying their application requirements and timing constraints. Software utilities can analyze real-time tasks and extract their characteristics and requirements for assisting the systems to guarantee schedulability. Real- time scheduling is the core of the real-time system design. It should allow real-time systems to exhibit predictable timing correctness regardless of possible uncertainty in run-time environments. In this dissertation, we study the problem of scheduling real-time tasks with resource and fault-tolerance requirements. For tasks with resource requirements, two types of platforms are examined: multiprocessor hard real-time systems and real-time database systems; for task with fault-tolerance requirements, we focus on hard real-time systems. We investigate preemptive priority-based scheduling for tasks with resource requirements in context of hard real-time systems. Rate-monotonic and earliest deadline first priority assignment strategies can meet deadlines if the schedulability conditions are satisfied. We propose resource control protocols, for these scheduling strategies, based on the concepts of priority inheritance and priority ceiling and describe schedulability conditions for meeting deadlines. Real-time database systems have different objectives for transaction scheduling. Minimizing miss ratio usually is the major concern. We study the significance of the knowledge of execution time in system performance and propose a class of optimistic concurrency control protocols using the knowledge of execution time. Our simulation results indicate that the knowledge of execution time substantially improve system performance. Fault-tolerance is an ability to maintain system in a safe and stable state such that the real-time application functions correctly and its timing constraints are satisfied even in the presence of faults. We develop a scheduling algorithm which attempts to build as many fault-tolerant tasks as possible into a schedule. We approximate system reliability by Markov chain models and illustrate the applicability of the proposed reliability models. We compare the proposed fault-tolerance scheduling approach with the basic fault-tolerance scheduling schemes and the simulation results show that our method provides better reliability than the basic scheduling schemes. (Also cross-referenced as UMIACS-TR-95-73

Memory efficient algorithm for solving the inverse gravimetry problem of finding several boundary surfaces in multilayered medium

For solving the inverse gravimetry problem of finding several boundary surfaces in a multilayered medium, the parallel algorithm was constructed and implemented for multicore CPU using OpenMP technology. The algorithm is based on the modified nonlinear conjugate gradient method with weighting factors previously proposed by authors. To reduce the memory requirements and computation time, the modification was constructed on the basis of utilizing the Toeplitz-block-Toeplitz structure of the Jacobian matrix of the integral operator. The model problem of reconstructing three surfaces using the quasi-real gravitational data was solved on a large grid. It was shown that the proposed implementation reduces the computation time by 80% in comparison with the earlier algorithm based on calculating the entire matrix. The parallel algorithm shows good scaling of 94% on 8-core processor. © 2019 Author(s).Ministry of Education and Science of the Republic of Kazakhstan: AP 05133873This work was financially supported by the Ministry of Education and Science of the Republic of Kazakhstan (project AP 05133873)

An IoT realization in an interdepartmental real time simulation lab for distribution system control and management studies

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Modern electric distribution systems with emerging operation methods and advanced metering systems bring new challenges to the system analysis, control and management. Interdependency of cyber and physical layers and interoperability of various control and management strategies require wide and accurate test and analysis before field implementation. Real-time simulation is known as a precise and reliable method to support new system/device development from initial design to implementation. However, for the study of different application algorithms, considering the various expertise requirements, the interconnection of multiple development laboratories to a real-time simulation lab, which constitutes the core of an interdepartmental real-time simulation platform, is needed. This paper presents the implemented architecture of such an integrated lab, which serves real-time simulations to different application fields within electric distribution system domain. The architecture is an implementation of an Internet-of-Things to facilitate software in-the-loop (SIL) and hardware in-the-loop (HIL) tests. A demo of the proposed architecture is presented, applied to the testing of a fault location algorithm in a portion of a realistic distribution system model. The implemented platform is flexible to integrate different algorithms in a plug-and-play fashion through a designed communication interface

Digital Twin for Real-time Li-ion Battery State of Health Estimation with Partially Discharged Cycling Data

To meet the fairly high safety and reliability requirements in practice, the state of health (SOH) estimation of Lithium-ion batteries (LIBs), which has a close relationship with the degradation performance, has been extensively studied with the widespread applications of various electronics. The conventional SOH estimation approaches with digital twin are end-of-cycle estimation that require the completion of a full charge/discharge cycle to observe the maximum available capacity. However, under dynamic operating conditions with partially discharged data, it is impossible to sense accurate real-time SOH estimation for LIBs. To bridge this research gap, we put forward a digital twin framework to gain the capability of sensing the battery's SOH on the fly, updating the physical battery model. The proposed digital twin solution consists of three core components to enable real-time SOH estimation without requiring a complete discharge. First, to handle the variable training cycling data, the energy discrepancy-aware cycling synchronization is proposed to align cycling data with guaranteeing the same data structure. Second, to explore the temporal importance of different training sampling times, a time-attention SOH estimation model is developed with data encoding to capture the degradation behavior over cycles, excluding adverse influences of unimportant samples. Finally, for online implementation, a similarity analysis-based data reconstruction has been put forward to provide real-time SOH estimation without requiring a full discharge cycle. Through a series of results conducted on a widely used benchmark, the proposed method yields the real-time SOH estimation with errors less than 1% for most sampling times in ongoing cycles.Comment: This paper has been accepted for IEEE Transactions on Industrial Informatic