5 research outputs found
Sensor Behavior Modeling and Algorithm Design for Intelligent Presence Detection in Nursery Rooms using iBeacon
This thesis is a part of a research project performed by two MS students Yang Yang and the author. The overall objective of the project is the design, implementation, and performance evaluation of algorithms for newborn localization and tracking in hospitals using Apple iBeacon technology. In the research project, I lead the path-loss modeling of iBeacon, design of algorithms for in-room presence detection system, and analysis of the accelerometer sensor. My partner, Yang Yang, leads the performance evaluation of the localization system using Cramer Rao Lower Bound (CRLB). This manuscript describes the project with a focus on my contributions in modeling the behavior of sensors and presence detection algorithms. Today, RFID detection is the most popular indoor detection technique. It provides high precision detection rate to distinguish the number of people in certain rooms of a building. However, special scanners and manual operations are required. This increases the cost and operation complexity. With the recent introduction of iBeacon by Apple, possibility of more efficient in-room presence detection has emerged for specific applications. An example of these applicatons is recording the number of visitors and newborns in a nursery room inside a hospital. The iBeacon uses Bluetooth Low Energy (BLE) technology for proximity broadcasting. Additionally, iBeacon carries a motion detection sensor, which can be utilized for counting the number of people and newborns entering and leaving a room. In this thesis we introduce a novel intelligent in-room presence detection system using iBeacon for the newborns in hospitals to determine the number of visitors and newborns\u27 location in the nursery room. We first develop a software application on iPhone to receive and extract the necessary data from iBeacon for further analysis. We build the path-loss model for the iBeacon based on the received signal strength (RSS) of the iBeacon, which is used for performance evaluation using CRLB in Yang Yang\u27s project. We also utilize the accelerometer in the smart phones to improve the performance of our detection system
Safe Control for Nonlinear Systems under Faults and Attacks via Control Barrier Functions
Safety is one of the most important properties of control systems. Sensor
faults and attacks and actuator failures may cause errors in the sensor
measurements and system dynamics, which leads to erroneous control inputs and
hence safety violations. In this paper, we improve the robustness against
sensor faults and actuator failures by proposing a class of Fault-Tolerant
Control Barrier Functions (FT-CBFs) for nonlinear systems. Our approach
maintains a set of state estimators according to fault patterns and
incorporates CBF-based linear constraints for each state estimator. We then
propose a framework for joint safety and stability by integrating FT-CBFs with
Control Lyapunov Functions. With a similar philosophy of utilizing redundancy,
we proposed High order CBF-based approach to ensure safety when actuator
failures occur. We propose a sum-of-squares (SOS) based approach to verify the
feasibility of FT-CBFs for both sensor faults and actuator failures. We
evaluate our approach via two case studies, namely, a wheeled mobile robot
(WMR) system in the presence of a sensor attack and a Boeing 747 lateral
control system under actuator failures.Comment: 15 pages, 5 figures, submitted to IEEE Transactions on Automatic
Contro
LQG Reference Tracking with Safety and Reachability Guarantees under Unknown False Data Injection Attacks
We investigate a linear quadratic Gaussian (LQG) tracking problem with safety
and reachability constraints in the presence of an adversary who mounts an FDI
attack on an unknown set of sensors. For each possible set of compromised
sensors, we maintain a state estimator disregarding the sensors in that set,
and calculate the optimal LQG control input at each time based on this
estimate. We propose a control policy which constrains the control input to lie
within a fixed distance of the optimal control input corresponding to each
state estimate. The control input is obtained at each time step by solving a
quadratically constrained quadratic program (QCQP). We prove that our policy
can achieve a desired probability of safety and reachability using the barrier
certificate method. Our control policy is evaluated via a numerical case study.Comment: 13 pages, 4 figures, transactio
Cooperative Perception for Safe Control of Autonomous Vehicles under LiDAR Spoofing Attacks
Autonomous vehicles rely on LiDAR sensors to detect obstacles such as
pedestrians, other vehicles, and fixed infrastructures. LiDAR spoofing attacks
have been demonstrated that either create erroneous obstacles or prevent
detection of real obstacles, resulting in unsafe driving behaviors. In this
paper, we propose an approach to detect and mitigate LiDAR spoofing attacks by
leveraging LiDAR scan data from other neighboring vehicles. This approach
exploits the fact that spoofing attacks can typically only be mounted on one
vehicle at a time, and introduce additional points into the victim's scan that
can be readily detected by comparison from other, non-modified scans. We
develop a Fault Detection, Identification, and Isolation procedure that
identifies non-existing obstacle, physical removal, and adversarial object
attacks, while also estimating the actual locations of obstacles. We propose a
control algorithm that guarantees that these estimated object locations are
avoided. We validate our framework using a CARLA simulation study, in which we
verify that our FDII algorithm correctly detects each attack pattern
IMM: An Imitative Reinforcement Learning Approach with Predictive Representation Learning for Automatic Market Making
Market making (MM) has attracted significant attention in financial trading
owing to its essential function in ensuring market liquidity. With strong
capabilities in sequential decision-making, Reinforcement Learning (RL)
technology has achieved remarkable success in quantitative trading.
Nonetheless, most existing RL-based MM methods focus on optimizing single-price
level strategies which fail at frequent order cancellations and loss of queue
priority. Strategies involving multiple price levels align better with actual
trading scenarios. However, given the complexity that multi-price level
strategies involves a comprehensive trading action space, the challenge of
effectively training profitable RL agents for MM persists. Inspired by the
efficient workflow of professional human market makers, we propose Imitative
Market Maker (IMM), a novel RL framework leveraging both knowledge from
suboptimal signal-based experts and direct policy interactions to develop
multi-price level MM strategies efficiently. The framework start with
introducing effective state and action representations adept at encoding
information about multi-price level orders. Furthermore, IMM integrates a
representation learning unit capable of capturing both short- and long-term
market trends to mitigate adverse selection risk. Subsequently, IMM formulates
an expert strategy based on signals and trains the agent through the
integration of RL and imitation learning techniques, leading to efficient
learning. Extensive experimental results on four real-world market datasets
demonstrate that IMM outperforms current RL-based market making strategies in
terms of several financial criteria. The findings of the ablation study
substantiate the effectiveness of the model components