CPU-GPU hybrid parallel binomial American option pricing

We present in this paper a novel parallel binomial algorithm that computes the price of an American option. The algorithm partitions a binomial tree constructed for the pricing into blocks of multiple levels of nodes, and assigns each such block to multiple processors. Each of the processors then computes the option's values at its assigned nodes in two phases. The algorithm is implemented and tested on a heterogeneous system consisting of an Intel multi-core processor and a NVIDIA GPU. The whole task is split and divided over and the CPU and GPU so that the computations are performed on the two processors simultaneously. In the hybrid processing, the GPU is always assigned the last part of a block, and makes use of a couple of buffers in the on-chip shared memory to reduce the number of accesses to the off-chip device memory. The performance of the hybrid processing is compared with an optimised CPU serial code, a CPU parallel implementation and a GPU standalone program.published_or_final_versio

A concurrent error detection based fault-tolerant 32 nm XOR-XNOR circuit implementation

As modern processors and semiconductor circuits move into 32 nm technologies and below, designers face the major problem of process variations. This problem makes designing VLSI circuits harder and harder, affects the circuit performance and introduces faults that can cause critical failures. Therefore, fault-tolerant design is required to obtain the necessary level of reliability and availability especially for safety-critical systems. Since XOR-XNOR circuits are basic building blocks in various digital and mixed systems, especially in arithmetic circuits, these gates should be designed such that they indicate any malfunction during normal operation. In fact, this property of verifying the results delivered by a circuit during its normal operation is called Concurrent Error Detection (CED). In this paper, we propose a CED based fault- tolerant XOR-XNOR circuit implementation. The proposed design is performed using the 32 nm process technology.published_or_final_versio

Big data encrypting transmission framework for a multi-AGV system

This project has developed a data transmission framework including Automated Guided Vehicles (AGVs), a cloud platform, edge computers and a master server. This framework is designed to provide a solution for protecting user privacy in the data transmission process of AGV in practical applications. The system divides the information collected by AGVs into messages and big data, where messages are transmitted through Message Queuing Telemetry Transport (MQTT) protocol and big data is transmitted through socket. Big data is collected by different sensors equipped in AGVs. Messages are processed by the AGV's main chip. Meanwhile, public key encryption based on RSA algorithm is performed in the transmission process to ensure the privacy and safety of information and users

Deep Learning-Based Multi-Step Solar Forecasting for PV Ramp-Rate Control Using Sky Images

Solar forecasting is one of the most promising approaches to address the intermit PV power generation by providing predictions before upcoming ramp events. In this paper, a novel multi-step forecasting (MSF) scheme is proposed for PV power ramp-rate control (PRRC). This method utilizes an ensemble of deep ConvNets without additional time-series models and exogenous variables, thus more suitable for industrial applications. The MSF strategy can make multiple predictions in comparison with a single forecasting point produced by a conventional method while maintaining the same high temporal resolution. Besides, stacked sky images that integrate temporal-spatial (ST) information of cloud motions are used to further improve the forecasting performance. The results demonstrate a favorable forecasting accuracy in comparison to the existing forecasting models with the highest skill score of 17.7%. In the PRRC application, the MSF-based PRRC can detect more ramp-rates violations with a higher control rate of 98.9% compared with the conventional forecasting based control. Thus, the PV generation can be effectively smoothed with less energy curtailment on both clear and cloudy days using the proposed approach

Mobile Robot Tracking with Deep Learning Models under the Specific Environments

Visual-based target tracking is one of the critical methodologies for the control problem of multi-robot systems. In dynamic mobile environments, it is common to lose the tracking targets due to partial visual occlusion. Technologies based on deep learning (DL) provide a natural solution to this problem. DL-based methods require less human intervention and fine-tuning. The framework has flexibility to be retrained with customized data sets. It can handle massive amounts of available video data in the target tracking system. This paper discusses the challenges of robot tracking under partial occlusion and compares the system performance of recent DL models used for tracking, namely you-only-look-once (YOLO-v5), Faster region proposal network (R-CNN) and single shot multibox detector (SSD). A series of experiments are committed to helping solve specific industrial problems. Four data sets are that cover various occlusion statuses are generated. Performance metrics of F1 score, precision, recall, and training time are analyzed under different application scenarios and parameter settings. Based on the metrics mentioned above, a comparative metric P is devised to further compare the overall performance of the three DL models. The SSD model obtained the highest P score, which was 13.34 times that of the Faster RCNN model and was 3.39 times that of the YOLOv5 model with the designed testing data set 1. The SSD model obtained the highest P scores, which was 11.77 times that of the Faster RCNN model and was 2.43 times that of the YOLOv5 model with the designed testing data set 2. The analysis reveals different characteristics of the three DL models. Recommendations are made to help future researchers to select the most suitable DL model and apply it properly in a system design.</jats:p

Towards the applicability of solar nowcasting: A practice on predictive PV power ramp-rate control

Solar forecasting has been widely adopted in modern power system operations to facilitate a reliable and continuous photovoltaic (PV) integration. Solar nowcasting, also known as intra-minute solar forecasting, is a new subdomain of solar forecasting. Nevertheless, despite the significant progress achieved in solar nowcasting over the last decade, one important aspect, that is, applicabilitydthe value and operability of nowcasts in practical grid operationsdis generally left out. To that end, this paper brings forth the applicability of solar nowcasting for the first time. Three time parameters involved in operational solar nowcasting are first identified, namely, forecast horizon, forecast resolution, and forecast model updating rate. Then paired with the state-of-the-art PV power ramp-rate control algorithm, i.e., predictive active power curtailment (PAPC), the effect of different time parameters is investigated, thus revealing the nowcasting applicability at large. Through four case studies and eight standardized deterministic and probabilistic solar nowcasting models, the applicability of solar nowcasting on PAPC is shown to be most characterized by the forecast horizon (up to a deviation of ramp smoothing rate around 12%, with smart persistence (SP) being the reference model), and least characterized by the forecast model updating rate (with a deviation of ramp smoothing rate less than 1% for SP). Moreover, the negatively-biased deter-ministic nowcasts and wider probabilistic nowcasts are found more applicable to PAPC. To promote solar nowcasting applicability on PAPC further, an outlook for future research is provided, from both a solar forecaster's and a system operator's viewpoints.(c) 2022 Elsevier Ltd. All rights reserved

Development of a reliability course for emerging circuits and systems

This paper presents a curriculum design of a course about reliability of circuits and systems. Contents in the learning modules include failure mechanisms of electronics, reliability for electronic components and circuit systems and simulation for circuit reliability. Through learning modules, students can learn concepts about reliability in circuits and systems, as well as develop awareness to design a reliable circuit system. © (2013) Trans Tech Publications, Switzerland.link_to_subscribed_fulltex