为营造学院良好的学术环境氛围，本周将举办学术交流汇报，供师生和学生之间相互交流讨论，具体安排如下。

日期：2020年10月15日（周四）

时间：16：00（下午四点）

地点：理科楼B311

汇报人：18级 软件工程 彭景盛

论文题目：

《A Fast Q-learning Based Data Storage Optimization for Low Latency in Data Center Networks》

论文简介：

Data storage optimizations (DS, e.g. low latency for data access) in data center net works(DCN) are diffificult online-making problems. Previously, they are done with heuristics under static network models which highly rely on designers’ understanding of the environment. Encouraged by recent successes in deep reinforcement learning techniques to solve intricate online assignment problems, we propose to use the Q-learning (QL) technique to train and learn from historical DS decisions, which can signifificantly reduce the data access delay. However, QL faces two challenges to be widely used in data centers. They are massive input data and the blindness on parameter settings which severely hamper the

convergence of the learning process. To solve these two key problems, we develop an evolutionary QL scheme, named as LFDS (Low latency and Fast convergence Data Storage). In the initial stage of the LFDS, the input matrix of QL is sparse to shrink the dimensionality of the massive input data while retaining its information as much as possible. In the following training phase, a specialized neural network is adopted to achieves a quick approximation. To overcome the blindness during QL training, the two key parameters, learning rate, and discount rate are carefully tested with real data input and network architecture. The preferred range of learning rate and discount rate are recommended for the use of QL in data centers, which brings high training rewards and fast convergence. Extensive simulations with real-world data show that the data access latency is decreased by 23.5% and the convergence rate is increased by 15%.

已被IEEE ACCESS录用.

汇报人：18级 软件工程 何彬永

论文题目：

《Soft Error Reliability Evaluation of Nanoscale Logic Circuits in the Presence of Multiple

Transient Faults》

论文简介：

Radiation-induced single transient faults (STFs) are expected to evolve into multiple transient faults (MTFs) at nanoscale CMOS technology nodes. For this reason, the reliability evaluation of logic circuits in the presence of MTFs is becoming an important aspect of the design process of deep submicron and nanoscale systems. However, an accurate evaluation of the reliability of large-scale and very large-scale circuits is both very complex and time-consuming. Accordingly, this paper presents a novel soft error reliability calculation

approach for logic circuits based on a probability distribution model. The correctness or incorrectness of individual logic elements are regarded as random events obeying Bernoulli

distribution. Subsequently, logic element conversion-based fault simulation experiments are conducted to analyze the logical masking effects of the circuit when one logic element fails or when two elements fail simultaneously. On this basis, the reliability boundaries of the logic circuits can efficiently be calculated using the proposed probability model and fault simulation results. The proposed solution can obtain an accurate reliability range through single fault and double faults simulations with small sample sizes, and also scales well with the variation of the error rate of the circuit element. To validate the proposed approach, we have calculated the reliability boundaries of ISCAS’85, ISCAS’89, and ITC’99 benchmark circuits. Statistical analysis and experimental results demonstrate that our method is effective and scalable, while also maintaining sufficiently close accuracy.

已被Journal of Electronic Testing录用.

汇报人：18级 软件工程 仝海昕

论文题目：

《An Ultra-Short-Term Electrical Load Forecasting Method Based on Temperature-Factor-Weight and LSTM Model》

论文简介：

Ultra-short-term electrical load forecasting is an important guarantee for the safety and effificiency of energy system operation. Temperature is also an important factor affecting the changes in electric load. However, in different cases, the impact of temperature on load forecasting will vary greatly, and sometimes even lead to the decrease of forecasting accuracy. This often brings great diffificulties to researchers’work. In order to make more scientifific use of temperature factor for ultra-short-term electrical load forecasting, especially to avoid the negative inflfluence of temperature on load forecasting, in this paper we propose an ultra-short-term electrical load forecasting method based on temperature factor weight and long short-term memory model. The proposed method evaluates the importance of the current prediction task’s temperature based on the change magnitude of the recent load and the correlation between temperature and load, and therefore the negative impacts of the temperature model can be avoided. The mean absolute percentage error of proposed method is decreased by 1.24%, 1.86%, and 6.21% compared with traditional long short-term memory model, back-propagation neural network, and gray model on average, respectively. The experimental results demonstrate that this method has obvious advantages in prediction accuracy and generalization ability.

已被energies录用.