The test generation of digital sequential circuits with the multiple observation time strategy

The test generation method is designed for digital circuits with memory on the basis of distinguishing state pairs of good and fault devices. The multiple observation time test strategy, 16-valued alphabet and genetic algorithms are used. The proposed method permits to cover the faults that are not detected with traditional methods. It increases the fault coverage.Для цифровых схем с памятью разработан метод построения тестов на основе различения пар состояний исправного и неисправного устройств. Применяется стратегия кратного наблюдения, 16-значный алфавит и генетические алгоритмы. Предложенный метод позволяет покрыть неисправности, являющиеся нетестируемыми традиционными методами. Это существенно повышает покрытие неисправностей.Для цифрових схем з пам’яттю розроблено метод побудови тестiв на базi розрiзнення пар станiв непошкодженого та пошкодженого пристроїв. Застосовано стратегiю кратного спостереження, 16-значний алфавiт та генетичнi алгоритми. Запропонований метод дозволяє покрити пошкодження, якi є нетестовними традицiйними методами. Це суттєво пiдвищує покриття пошкоджень

Scheduling in TSN networks using machine learning

The massive adoption of Ethernet technology in multiple sectors, produces the need to provide deterministic solutions to ensure a Quality of Service (QoS) that meets the requirements of time-triggered flows. For this, the Time-Sensitive Networking (TSN) Task Group (TG) of the IEEE 802.1 developed a set of standards that define mechanisms for time-sensitive transmissions of data over Ethernet networks. This project focuses on studying the feasibility of scheduling three classes of time-triggered flows with different time constraints over a simple network topology, which is made from two TSN (Time-Sensitive Networking) nodes connected through a link. Scheduling multiple time-triggered flows is a complex problem because the scheduling, if exists, must meet the time constraints of all these flows. To address this challenge, we explore the potential of using supervised machine learning classification models to accurately predict the feasibility of scheduling a given set of time-triggered flows, meeting their time-constraints, in a Time-Sensitive Network (TSN). Supervised models require a training dataset that contains a data matrix and a class label vector. To obtain the class label vector of each observation, we use an adaptation of the implementation developed in [27] of the Integer Linear Programming (ILP) model introduced in [33]. Two different models are considered: K-Nearest Neighbours (K-NN) and Support Vector Machine (SVM). These algorithms are tested and built from the application of the Leave One Out Cross-Validation (LOOCV) technique with the generated datasets, and the results obtained are compared and discussed. Finally, a hybrid verification strategy is proposed to train and test machine learning models, drastically reducing the resources and computation time originally required to compute the class label of each observation of the dataset

Hierarchical Flexibility Offering Strategy for Integrated Hybrid Resources in Real-time Energy Markets

This paper proposes a hierarchical model for determining the energy flexibility offering strategy of integrated hybrid resources (IHRs) in power distribution systems to participate in real-time energy markets. The proposed model utilizes the scalability, fast response time, and uncertainty observation of deep reinforcement learning (DRL) to overcome the scalability issue of operating numerous flexible resources and deliverability of energy flexibility to the real-time markets in the presence of the network constraints. To that end, the power distribution system is divided into multiple IHRs, where different types of flexible loads, energy storage systems, and solar plants with controllable inverters are operated through local IHR controllers, trained by deep deterministic policy gradient (DDPG) algorithm. Active power request and reactive power capacity of IHRs are then transmitted to a central flexibility controller, where a quadratic optimization model ensures the deliverability of the energy flexibility to the real-time energy market by satisfying the distribution network constraints. The proposed model is implemented on the 123-bus test power distribution system, demonstrating the capability of DRL-based hierarchical model for scalable operation of IHRs in order to offer deliverable energy flexibility to the real-time energy market

Testing for Multiple Bubbles 1: Historical Episodes of Exuberance and Collapse in the S&P 500

Published in International Economic Review, https://doi.org/10.1111/iere.12132</p

Testing for Multiple Bubbles 2: Limit Theory of Real Time Detectors

Singapore MOE Academic Research Tier 2Published in International Economic Review, https://doi.org/10.1111/iere.12131</p

THE EFFECT OF CURRENT RATIO, DEBT TO EQUITY RATIO, AND NET PROFIT MARGIN ON STOCK RETURNS

The aims of this study is to simultaneously and partially test and analyze the impact of the Current Ratio, Debt to Equity Ratio, and Net Profit Margin on stock returns. This study's population consisted of 11 pharmaceutical companies registered on the Indonesia Stock Exchange between 2018 and 2022. This study's sample was determined using a purposive sampling strategy, yielding a sample of 9 pharmaceutical companies with a total of 35 observation data. Multiple linear regression analysis was employed as the analytical method. The study's findings reveal that the Current Ratio, Debt to Equity Ratio, and Net Profit Margin all have an impact on stock returns at the same time. This suggests that increasing the three variables, namely the Current Ratio and Net Profit Margin while decreasing the Debt to Equity Ratio, will enhance stock returns. The current ratio has no statistically significant impact on stock returns. The debt-to-equity ratio has no substantial effect on stock returns. Meanwhile, Net Profit Margin has a minor impact on Stock Returns. Stock returns in pharmaceutical businesses listed on the Indonesian Stock Exchange will rise as Net Present Value rises