DxNAT - Deep Neural Networks for Explaining Non-Recurring Traffic Congestion

Non-recurring traffic congestion is caused by temporary disruptions, such as accidents, sports games, adverse weather, etc. We use data related to real-time traffic speed, jam factors (a traffic congestion indicator), and events collected over a year from Nashville, TN to train a multi-layered deep neural network. The traffic dataset contains over 900 million data records. The network is thereafter used to classify the real-time data and identify anomalous operations. Compared with traditional approaches of using statistical or machine learning techniques, our model reaches an accuracy of 98.73 percent when identifying traffic congestion caused by football games. Our approach first encodes the traffic across a region as a scaled image. After that the image data from different timestamps is fused with event- and time-related data. Then a crossover operator is used as a data augmentation method to generate training datasets with more balanced classes. Finally, we use the receiver operating characteristic (ROC) analysis to tune the sensitivity of the classifier. We present the analysis of the training time and the inference time separately

Ku band rotary traveling-wave voltage controlled oscillator

Voltage-controlled oscillator (VCO) plays a key role in determination of the link budget of wireless communication, and consequently the performance of the transceiver. Lowering the noise contribution from the VCO to the entire system is always challenging and remains the active research area. Motivated by high demands for the low-phase noise, low-power consumption VCO in the application of 5G, radar-sensing system, implantable device, to name a few, this research focused on the design of a rotary travelling-wave oscillator (RTWO). A power conscious RTWO with reliable direction control of the wave propagation was investigated. The phase noise was analyzed based on the proposed RTWO. The phase noise reduction technique was introduced by using tail current source filtering technique in which a figure-8 inductors were employed. Three RTWO were implemented based on GF 130 nm standard CMOS process and TSMC 130 nm standard CMOS process. The first design was achieving 16-GHz frequency with power consumption of 5.8-mW with 190.3 dBc/Hz FoM at 1 MHz offset. The second and third design were operating at 14-GHz with a power consumption range of 13-18.4mW and 14.6-20.5mW, respectively. The one with filtering technique achieved FoM of 184.8 dBc/Hz at 1 MHz whereas the one without inudctor filtering obtained FoM of 180.8 dBc/Hz at 1 MHz offset based on simulation

Time-Delayed Data Informed Reinforcement Learning for Approximate Optimal Tracking Control

This paper proposes a time-delayed data informed reinforcement learning method, referred as incremental adaptive dynamic programming, to learn approximate solutions to optimal tracking control problems (OTCPs) of high-dimensional nonlinear systems. Departing from available solutions to OTCPs, our developed tracking control scheme settles the curse of complexity problem in value function approximation from a decoupled way, circumvents the learning inefficiency regarding varying desired trajectories by avoiding introducing a reference trajectory dynamics into the learning process, and requires neither an accurate nor identified dynamics using time-delayed signals. Specifically, the intractable OTCP of a high-dimensional uncertain system is first converted into multiple manageable sub-OTCPs of low-dimensional incremental subsystems constructed using time-delayed data. Then, the resulting sub-OTCPs are approximately solved by a parallel critic learning structure. The proposed tracking control scheme is developed with rigorous theoretical analysis of system stability and weight convergence, and validated experimentally on a 3-DoF robot manipulator

Guided Policy Search for Sequential Multitask Learning

Policy search in reinforcement learning (RL) is a practical approach to interact directly with environments in parameter spaces, that often deal with dilemmas of local optima and real-time sample collection. A promising algorithm, known as guided policy search (GPS), is capable of handling the challenge of training samples using trajectory-centric methods. It can also provide asymptotic local convergence guarantees. However, in its current form, the GPS algorithm cannot operate in sequential multitask learning scenarios. This is due to its batch-style training requirement, where all training samples are collectively provided at the start of the learning process. The algorithm's adaptation is thus hindered for real-time applications, where training samples or tasks can arrive randomly. In this paper, the GPS approach is reformulated, by adapting a recently proposed, lifelong-learning method, and elastic weight consolidation. Specifically, Fisher information is incorporated to impart knowledge from previously learned tasks. The proposed algorithm, termed sequential multitask learning-GPS, is able to operate in sequential multitask learning settings and ensuring continuous policy learning, without catastrophic forgetting. Pendulum and robotic manipulation experiments demonstrate the new algorithms efficacy to learn control policies for handling sequentially arriving training samples, delivering comparable performance to the traditional, and batch-based GPS algorithm. In conclusion, the proposed algorithm is posited as a new benchmark for the real-time RL and robotics research community