On Learning based Parameter Calibration and Ramp Metering of freeway Traffic Systems

Ph.DDOCTOR OF PHILOSOPH

Artificial intelligence-based human–computer interaction technology applied in consumer behavior analysis and experiential education

In the course of consumer behavior, it is necessary to study the relationship between the characteristics of psychological activities and the laws of behavior when consumers acquire and use products or services. With the development of the Internet and mobile terminals, electronic commerce (E-commerce) has become an important form of consumption for people. In order to conduct experiential education in E-commerce combined with consumer behavior, courses to understand consumer satisfaction. From the perspective of E-commerce companies, this study proposes to use artificial intelligence (AI) image recognition technology to recognize and analyze consumer facial expressions. First, it analyzes the way of human–computer interaction (HCI) in the context of E-commerce and obtains consumer satisfaction with the product through HCI technology. Then, a deep neural network (DNN) is used to predict the psychological behavior and consumer psychology of consumers to realize personalized product recommendations. In the course education of consumer behavior, it helps to understand consumer satisfaction and make a reasonable design. The experimental results show that consumers are highly satisfied with the products recommended by the system, and the degree of sanctification reaches 93.2%. It is found that the DNN model can learn consumer behavior rules during evaluation, and its prediction effect is increased by 10% compared with the traditional model, which confirms the effectiveness of the recommendation system under the DNN model. This study provides a reference for consumer psychological behavior analysis based on HCI in the context of AI, which is of great significance to help understand consumer satisfaction in consumer behavior education in the context of E-commerce

Design of the Tsinghua Tabletop Kibble Balance

The Kibble balance is a precision instrument for realizing the mass unit, the kilogram, in the new international system of units (SI). In recent years, an important trend for Kibble balance experiments is to go tabletop, in which the instrument's size is notably reduced while retaining a measurement accuracy of $10^{-8}$. In this paper, we report a new design of a tabletop Kibble balance to be built at Tsinghua University. The Tsinghua Kibble balance aims to deliver a compact instrument for robust mass calibrations from 10 g to 1 kg with a targeted measurement accuracy of 50 $\mu$g or less. Some major features of the Tsinghua Kibble balance system, including the design of a new magnet, one-mode measurement scheme, the spring-compensated magnet moving mechanism, and magnetic shielding considerations, are discussed.Comment: 8 pages, 9 figure

(E)-2-[2-(4-Chloro­benzyl­idene)hydrazin­yl]-4-[3-(morpholin-4-ium-4-yl)propyl­amino]­quinazolin-1-ium bis­(perchlorate)

In the title compound, C22H27ClN6O2 2+·2ClO4 −, the mol­ecule adopts an E conformation about the C=N double bond. The quinazoline ring is approximately planar, with an r.m.s. deviation of 0.0432 Å, and forms a dihedral angle of 5.77 (4)° with the chloro­phenyl ring. The crystal packing features N—H⋯O hydrogen bonds

Improved Side Channel Cube Attacks on PRESENT

The paper presents several improved side channel cube attacks on PRESENT based on single bit leakage model. Compared with the previous study of Yang et al in CANS 2009 [30], based on the same model of single bit leakage in the 3rd round, we show that: if the PRESENT cipher structure is unknown, for the leakage bit 0, 32-bit key can be recovered within $2^{7.17}$ chosen plaintexts; if the cipher structure is known, for the leakage bit 4,8,12, 48-bit key can be extracted by $2^{11.92}$ chosen plaintexts, which is less than $2^{15}$ in [30]; then, we extend the single bit leakage model to the 4th round, based on the two level “divide and conquer” analysis strategy, we propose a sliding window side channel cube attack on PRESENT, for the leakage bit 0, about $2^{15.14}$ chosen plaintexts can obtain 60-bit key; in order to obtain more key bits, we propose an iterated side channel cube attack on PRESENT, about $2^{8.15}$ chosen plaintexts can obtain extra 12 equivalent key bits, so overall $2^{15.154}$ chosen plaintexts can reduce the PRESENT-80 key searching space to $2^{8}$; finally, we extend the attack to PRESENT-128, about $2^{15.156}$ chosen plaintexts can extract 85 bits key, and reduce the PRESENT-128 key searching space to $2^{43}$. Compared with the previous study of Abdul-Latip et al in ASIACCS 2011 [31] based on the Hamming weight leakage model, which can extract 64-bit key of PRESENT-80/128 by $2^{13}$ chosen plaintexts, our attacks can extract more key bits, and have certain advantages over [31]