Effective LiDAR Damage Detection:Comparing Two Detection Algorithms

The health conditions of highway bridges is critical for sustained transportation operations.US federal government mandates that all bridges built with public funds are to be inspected visually every two years. There is a growing consensus that additional rapid and non-intrusive methods for bridge damage evaluation are needed.This paper explores the potential of applying ground-based laser scanners for bridge damage evaluation. LiDAR has the potential of providing high-density,full-field surface static imaging.Hence,it can generate volumetric quantification of concrete corrosion or steel erosion.By recording object surface topology,LiDAR can detect different damages on the bridge structure and differentiate damage types according to the surface flatness and smoothness.To determine the effectiveness of LiDAR damage detection,two damage detection algorithms are presented and compared using scans on actual bridge damages.The results demonstrate and validate LiDAR damage quantification,which can be a powerful tool for bridge condition evaluation.

Robust sliding mode control of general time-varying delay stochastic systems with structural uncertainties

This paper presents a new robust sliding mode control （SMC） method with well-developed theoretical proof for general uncertain time-varying delay stochastic systems with structural uncertainties and the Brownian noise （Wiener process）. The key features of the proposed method are to apply singular value decomposition （SVD） to all structural uncertainties and to introduce adjustable parameters for control design along with the SMC method. It leads to a less-conservative condition for robust stability and a new robust controller for the general uncertain stochastic systems via linear matrix inequality （LMI） forms. The system states are able to reach the SMC switching surface as guaranteed in probability 1. Furthermore, it is theoretically proved that the proposed method with the SVD and adjustable parameters is less conservatism than the method without the SVD. The paper is mainly to provide all strict theoretical proofs for the method and results.

An association rule based approach to reducing visual clutter in parallel sets

Although Parallel Sets,a popular categorical data visualization technique,intuitively reveals the frequency based relationships in details,a high-dimensional categorical dataset brings a cluttered visual display that seriously obscures the relationship explorations.Association rule mining is a popular approach to discovering relationships among categorical variables.It could complement Parallel Sets to group ribbons in a meaningful way.However,it is difficult to understand a larger number of rules discovered from a high-dimensional categorical dataset.In this paper,we integrate the two approaches into a visual analytics system for exploring high-dimensional categorical data with dichotomous outcome.The system not only helps users interpret association rules intuitively,but also provides an effective dimension and category reduction approach towards a less clustered and more organized visualization.The effectiveness and efficiency of our approach are illustrated by a set of user studies and experiments with benchmark datasets.

Fast Processing of Optical Fringe Movement in Displacement Sensors Without Using an ADC

An interferometer based optical sensor for displacement measurement is reported. This method requires quite simple signal processing as well as least electronic components. Referring to this technique, two photodiodes spatially shifted by 90 degrees were used. The output of photodiodes was converted into rectangular signals which were extracted in LabVIEW using the data acquisition card without using an analog to digital converters （ADC）. We have also processed the signals in C＋＋ after acquiring via parallel port. A Michelson interferometer configuration was used to produce linear fringes for the detection of displacements. The displacement less than 100nm could be measured using this technique.

RiskVA:A Visual Analytics System for Consumer Credit Risk Analysis

Consumer credit risk analysis plays a significant role in stabilizing a bank＇s investments and in maximizing its profits. As a large financial institution, Bank of America relies on effective risk analyses to minimize the net credit loss resulting from its credit products （e.g., mortgage and credit card loans）. Due to the size and complexity of the data involved in this process, analysts are facing challenges in monitoring the data, comparing its geospatial and temporal patterns, and developing appropriate strategies based on the correlation from multiple analysis perspectives. To address these challenges, we present RiskVA, an interactive visual analytics system that is tailored to support credit risk analysis. RiskVA provides interactive data exploration and correlation, and visually facilitates depictions of market fluctuations and temporal trends for a targeted credit product. When evaluated by analysts from Bank of America, RiskVA was appreciated for its effectiveness in facilitating the bank＇s risk management.

Unlearning Descartes:Sentient AI is a Political Problem

The emergence of Large Language Models(LLMs)has renewed debate about whether Artificial Intelligence(AI)can be conscious or sentient.This paper identifies two approaches to the topic and argues:(1)A“Cartesian”approach treats consciousness,sentience,and personhood as very similar terms,and treats language use as evidence that an entity is conscious.This approach,which has been dominant in AI research,is primarily interested in what consciousness is,and whether an entity possesses it.(2)An alternative“Hobbesian”approach treats consciousness as a sociopolitical issue and is concerned with what the implications are for labeling something sentient or conscious.This both enables a political disambiguation of language,consciousness,and personhood and allows regulation to proceed in the face of intractable problems in deciding if something“really is”sentient.(3)AI systems should not be treated as conscious,for at least two reasons:(a)treating the system as an origin point tends to mask competing interests in creating it,at the expense of the most vulnerable people involved;and(b)it will tend to hinder efforts at holding someone accountable for the behavior of the systems.A major objective of this paper is accordingly to encourage a shift in thinking.In place of the Cartesian question-is AI sentient?-I propose that we confront the more Hobbesian one:Does it make sense to regulate developments in which AI systems behave as if they were sentient?

Forced Collision:Detecting Wormhole Attacks with Physical Layer Network Coding

Previous research on security of network coding focused on the protection of data dissemination procedures and the detection of malicious activities such as pollution attacks. The capabilities of network coding to detect other attacks have not been fully explored. In this paper, we propose a new mechanism based on physical layer network coding to detect wormhole attacks. When two signal sequences collide at the receiver, the starting point of the collision is determined by the distances between the receiver and the senders. Therefore, by comparing the starting points of the collisions at two receivers, we can estimate the distance between them and detect fake neighbor connections via wormholes. While the basic idea is clear, we have proposed several schemes at both physical and network layers to transform the idea into a practical approach. Simulations using BPSK modulation at the physical layer show that the wireless nodes can effectively detect fake neighbor connections without the adoption of special hardware or time synchronization.