Browsing by Author "Chien, Stanley Yung-Ping"

Now showing 1 - 9 of 9
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    Advanced natural language processing and temporal mining for clinical discovery
    (2015-08-17) Mehrabi, Saeed; Jones, Josette F.; Palakal, Mathew J.; Chien, Stanley Yung-Ping; Liu, Xiaowen; Schmidt, C. Max
    There has been vast and growing amount of healthcare data especially with the rapid adoption of electronic health records (EHRs) as a result of the HITECH act of 2009. It is estimated that around 80% of the clinical information resides in the unstructured narrative of an EHR. Recently, natural language processing (NLP) techniques have offered opportunities to extract information from unstructured clinical texts needed for various clinical applications. A popular method for enabling secondary uses of EHRs is information or concept extraction, a subtask of NLP that seeks to locate and classify elements within text based on the context. Extraction of clinical concepts without considering the context has many complications, including inaccurate diagnosis of patients and contamination of study cohorts. Identifying the negation status and whether a clinical concept belongs to patients or his family members are two of the challenges faced in context detection. A negation algorithm called Dependency Parser Negation (DEEPEN) has been developed in this research study by taking into account the dependency relationship between negation words and concepts within a sentence using the Stanford Dependency Parser. The study results demonstrate that DEEPEN, can reduce the number of incorrect negation assignment for patients with positive findings, and therefore improve the identification of patients with the target clinical findings in EHRs. Additionally, an NLP system consisting of section segmentation and relation discovery was developed to identify patients' family history. To assess the generalizability of the negation and family history algorithm, data from a different clinical institution was used in both algorithm evaluations.
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    Design and Implementation of Energy Usage Monitoring and Control Systems Using Modular IIOT Framework
    (2021-05) Chheta, Monil Vallabhbhai; Chien, Stanley Yung-Ping; Chen, Jie; Li, Lingxi
    This project aims to develop a cloud-based platform that integrates sensors with business intelligence for real-time energy management at the plant level. It provides facility managers, an energy management platform that allows them to monitor equipment and plant-level energy consumption remotely, receive a warning, identify energy loss due to malfunction, present options with quantifiable effects for decision-making, and take actions, and assess the outcomes. The objectives consist of: 1. Developing a generic platform for the monitoring energy consumption of industrial equipment using sensors 2. Control the connected equipment using an actuator 3. Integrating hardware, cloud, and application algorithms into the platform 4. Validating the system using an Energy Consumption Forecast scenario A Demo station was created for testing the system. The demo station consists of equip- ment such as air compressor, motor and light bulb. The current usage of these equipment is measured using current sensors. Apart from current sensors, temperature sensor, pres- sure sensor and CO2 sensor were also used. Current consumption of these equipment was measured over a couple of days. The control system was tested randomly by turning on equipment at random times. Turning on the equipment resulted in current consumption which ensured that the system is running. Thus, the system worked as expected and user could monitor and control the connected equipment remotely.
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    Development of a Mechatronics Instrument Assisted Soft Tissue Mobilization (IASTM) Device to Quantify Force and Orientation Angles
    (2016-05) Alotaibi, Ahmed Mohammed; Anwar, Sohel; Loghmani, Mary T.; Chien, Stanley Yung-Ping
    Instrument assisted soft tissue mobilization (IASTM) is a form of massage using rigid manufactured or cast devices. The delivered force, which is a critical parameter in massage during IASTM, has not been measured or standardized for most clinical practices. In addition to the force, the angle of treatment and frequency play an important role during IASTM. As a result, there is a strong need to characterize the delivered force to a patient, angle of treatment, and stroke frequency. This thesis proposes two novel mechatronic designs for a specific instrument from Graston Technique(Model GT3), which is a frequently used tool to clinically deliver localize pressure to the soft tissue. The first design is based on compression load cells, where 4-load cells are used to measure the force components in three-dimensional space. The second design uses a 3D load cell, which can measure all three force components force simultaneously. Both designs are implemented with IMUduino microcontroller chips which can also measure tool orientation angles and provide computed stroke frequency. Both designs, which were created using Creo CAD platform, were also analyzed thorough strength and integrity using the finite element analysis package ANSYS. Once the static analysis was completed, a dynamic model was created for the first design to simulate IASTM practice using the GT-3 tool. The deformation and stress on skin were measured after applying force with the GT-3 tool. Additionally, the relationship between skin stress and the load cell measurements has been investigated. The second design of the mechatronic IASTM tool was validated for force measurements using an electronic plate scale that provided the baseline force values to compare with the applied force values measured by the tool. The load cell measurements and the scale readings were found to be in agreement within the expected degree of accuracy. The stroke frequency was computed using the force data and determining the peaks during force application. The orientation angles were obtained from the built-in sensors in the microchip.
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    Energy Optimization of Heating, Ventilation, and Air Conditioning Systems
    (2024-05) Taheri, Saman; Razban, Ali; Chen, Jie; Du, Xiaoping; Chien, Stanley Yung-Ping
    The energy consumption in the building sector is responsible for over 36% of the total energy consumption across the globe. Of all the energy-consumer devices within a building, heating, ventilation, and air conditioning (HVAC) systems account for over 50% of the total energy consumed. This makes HVAC systems a source of preventable and unexplored energy waste that can be tackled by incorporating intelligent operations. Since its inception, model predictive control (MPC) has been one of the prospective solutions for HVAC management systems to reduce both costs and energy usage. Additionally, MPC is becoming increasingly practical as the processing capacity of building automation systems increases and a large quantity of monitored building data becomes available. MPC also provides the potential to improve the energy efficiency of HVAC systems via its capacity to consider limitations, to predict disruptions, and to factor in multiple competing goals such as interior thermal comfort and building energy consumption. In this regard, the opening chapter delves into the evolving landscape of the HVAC industry. It explores how rapid advancements in technology, growing concerns about climate change, and the ever-present need for energy efficiency are driving innovation. The chapter highlights the shift from static to dynamic HVAC systems, where buildings become sensor rich networks enabling advanced control strategies like Model Predictive Control (MPC) and Fault Detection and Diagnosis (FDD). we first provide a comprehensive review of the literature concerning the application of MPC in HVAC systems. Detailed discussions of modeling approaches and optimization algorithms are included. Numerous design aspects such as prediction horizon, time step, and cost function, that impact MPC performance are discussed in detail. The technical characteristics, advantages, and disadvantages of various types of modeling software are discussed. Next, a thorough, real-world case study for the design and implementation of a generalized data-collection and control architecture for HVAC systems in an educational building is proposed. The proposed MPC method adds a supervisory control layer on top of the current BMS by delivering temperature setpoints to the legacy controller. This means that the technique may be used to a variety of current HVAC systems in different commercial buildings. In addition, the utilization of remote web services to host the cloud-based architecture significantly minimizes the amount of technical expertise generally necessary to create such systems. In addition, we provide significant lessons learned from the installation process and we list indicative prices, therefore minimizing uncertainty for other researchers and promoting the use of comparable solutions. Chapter two focuses on Fault Detection and Diagnosis (FDD), a critical component of maintaining optimal HVAC performance and minimizing energy waste. HVAC systems are susceptible to malfunctions over time, leading to increased energy consumption and higher maintenance costs. FDD techniques play a vital role in identifying and diagnosing these faults early on, allowing for timely repairs and preventing further deterioration. This chapter introduces a novel bi-level machine learning framework for diagnosing faults in air handling units. This framework addresses key challenges associated with FDD. A bi-level machine learning framework is developed for diagnosing faults in air handling units (AHUs) and rooftop units (RTUs) based on principal component analysis (PCA), time series anomaly detection, and random forest (RF). By proposing this framework, we address three persistent challenges in this field: (I) minimizing false positives; (II) accounting for data imbalance; and (III) normal condition monitoring of equipment. It is shown that PCA can reduce the dataset dimension with one principal component accounting for 95% of data variance. Also, the random forest could classify the faults with 89% precision for single-zone AHU, 85% precision for RTU, and 79% for multi-zone AHU. Chapter three tackles the practical implementation of Model Predictive Control (MPC) in a real-world commercial building setting. It details the development, implementation, and cost analysis of a universally applicable cloud-based MPC framework for HVAC control systems. This chapter offers valuable insights into the feasibility and effectiveness of MPC in achieving energy efficiency goals while maintaining occupant comfort. The chapter delves into the hardware and software components used for data acquisition and MPC implemen tation. It emphasizes the use of cloud-based microservices to ensure seamless integration with existing building management systems, promoting wider adoption of this advanced control strategy. Three innovative control strategies are presented and evaluated in this chapter. The chapter presents compelling evidence for the effectiveness of these strategies, showcasing significant energy savings of up to 19.21%. Chapter four focuses on Occupancy-based Demand Controlled Ventilation (DCV) as a means to optimize indoor air quality (IAQ) while minimizing energy consumption. This chapter highlights the growing importance of IAQ in the wake of the COVID-19 pandemic and its impact on occupant health and well-being. Current ventilation standards often rely on static occupancy assumptions, which can lead to over-ventilation during unoccupied pe riods and wasted energy. This chapter proposes a dynamic occupant behavior model using machine learning algorithms to predict CO2 concentrations within buildings. The chapter investigates the performance of various machine learning algorithms, ultimately identify ing a Multilayer Perceptron (MLP) as the most effective in predicting CO2 levels under dynamic occupancy conditions. This model allows for real-time modulation of ventilation rates, ensuring adequate IAQ while minimizing energy consumption. The concluding chapter presents experimental findings on the effectiveness of adaptive Variable Frequency Drive (VFD) control strategies in optimizing HVAC energy consump tion. Variable Frequency Drives allow for adjusting the speed of electric motors, including those powering HVAC fans. This chapter explores the potential of using real-time occu pancy predictions to optimize VFD operation. The proposed control strategy demonstrates impressive energy savings, achieving a 51.4% reduction in HVAC fan energy consumption while adhering to ASHRAE IAQ standards. This chapter paves the way for occupant-centric ventilation strategies that prioritize both human health and energy efficiency. These results underscore the potential of predictive control systems to transform building operations to ward greater sustainability and efficiency. The chapter acknowledges the need for further validation through extended monitoring and analysis. In summary, this thesis contributes significantly to the advancement of smart building technologies by proposing practical frameworks for implementing advanced control strategies in HVAC systems. The findings presented here offer valuable insights for building designers, engineers, facility managers, and policymakers interested in creating sustainable, energy efficient, and occupant-centric buildings. The developed frameworks have the potential to be applied across a wide range of building types and climatic conditions, promoting broader adoption of smart building technologies and contributing to a more sustainable built envi ronment.
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    Identification of unknown petri net structures from growing observation sequences
    (2015-06-08) Ruan, Keyu; Li, Lingxi; King, Brian; Chien, Stanley Yung-Ping
    This thesis proposed an algorithm that can find optimized Petri nets from given observation sequences according to some rules of optimization. The basic idea of this algorithm is that although the length of the observation sequences can keep growing, we can think of the growing as periodic and algorithm deals with fixed observations at different time. And the algorithm developed has polynomial complexity. A segment of example code programed according to this algorithm has also been shown. Furthermore, we modify this algorithm and it can check whether a Petri net could fit the observation sequences after several steps. The modified algorithm could work in constant time. These algorithms could be used in optimization of the control systems and communication networks to simplify their structures.
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    Pedestrian Protection Using the Integration of V2V Communication and Pedestrian Automatic Emergency Braking System
    (2015-12-01) Tang, Bo; Chien, Stanley Yung-Ping; Chen, Yaobin; Li, Lingxi; King, Brian
    The Pedestrian Automatic Emergency Braking System (PAEB) can utilize on-board sensors to detect pedestrians and take safety related actions. However, PAEB system only benefits the individual vehicle and the pedestrians detected by its PAEB. Additionally, due to the range limitations of PAEB sensors and speed limitations of sensory data processing, PAEB system often cannot detect or do not have sufficient time to respond to a potential crash with pedestrians. For further improving pedestrian safety, we proposed the idea for integrating the complimentary capabilities of V2V and PAEB (V2V-PAEB), which allows the vehicles to share the information of pedestrians detected by PAEB system in the V2V network. So a V2V-PAEB enabled vehicle uses not only its on-board sensors of the PAEB system, but also the received V2V messages from other vehicles to detect potential collisions with pedestrians and make better safety related decisions. In this thesis, we discussed the architecture and the information processing stages of the V2V-PAEB system. In addition, a comprehensive Matlab/Simulink based simulation model of the V2V-PAEB system is also developed in PreScan simulation environment. The simulation result shows that this simulation model works properly and the V2V-PAEB system can improve pedestrian safety significantly.
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    A petri net based model for AEB systems considering vehicle and pedestrian/cyclist in a certain area
    (2017) Niu, Wensen; Chien, Stanley Yung-Ping; Li, Lingxi; Anwar, Sohel
    For AEB performance testing, surrogates and testing systems have been developed by the Transportation Active Safety Institute (TASI). A set of tests, both for performance of vehicle equipped with AEB systems and for harmonization of surrogates, have been conducted under different scenarios with different variables which include cyclist speed, vehicle speed, cyclist moving directions, and so on. There are several braking patterns described in this thesis, which provide the possibility to control the distance of an emergency braking. With different braking distance and steering angle during the emergency braking, choosing the final place of a vehicle becomes possible. This thesis considers vehicle and pedestrian/cyclist together to avoid a crash in a certain area rather than predicting the collision point. Petri net models were built for both vehicle and pedestrian/cyclist in potential collision area and crossing road scenarios. Then, controllers were designed for Petri net models and all possible states were calculated.
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    Plant Level IIoT Based Energy Management Framework
    (2023-05) Koshy, Liya Elizabeth; Chien, Stanley Yung-Ping; Chen, Jie; King, Brian
    The Energy Monitoring Framework, designed and developed by IAC, IUPUI, aims to provide a cloud-based solution that combines business analytics with sensors for real-time energy management at the plant level using wireless sensor network technology. The project provides a platform where users can analyze the functioning of a plant using sensor data. The data would also help users to explore the energy usage trends and identify any energy leaks due to malfunctions or other environmental factors in their plant. Additionally, the users could check the machinery status in their plant and have the capability to control the equipment remotely. The main objectives of the project include the following: • Set up a wireless network using sensors and smart implants with a base station/ controller. • Deploy and connect the smart implants and sensors with the equipment in the plant that needs to be analyzed or controlled to improve their energy efficiency. • Set up a generalized interface to collect and process the sensor data values and store the data in a database. • Design and develop a generic database compatible with various companies irrespective of the type and size. • Design and develop a web application with a generalized structure. Hence the database can be deployed at multiple companies with minimum customization. The web app should provide the users with a platform to interact with the data to analyze the sensor data and initiate commands to control the equipment. The General Structure of the project constitutes the following components: • A wireless sensor network with a base station. • An Edge PC, that interfaces with the sensor network to collect the sensor data and sends it out to the cloud server. The system also interfaces with the sensor network to send out command signals to control the switches/ actuators. • A cloud that hosts a database and an API to collect and store information. • A web application hosted in the cloud to provide an interactive platform for users to analyze the data. The project was demonstrated in: • Lecture Hall (https://iac-lecture-hall.engr.iupui.edu/LectureHallFlask/). • Test Bed (https://iac-testbed.engr.iupui.edu/testbedflask/). • A company in Indiana. The above examples used sensors such as current sensors, temperature sensors, carbon dioxide sensors, and pressure sensors to set up the sensor network. The equipment was controlled using compactable switch nodes with the chosen sensor network protocol. The energy consumption details of each piece of equipment were measured over a few days. The data was validated, and the system worked as expected and helped the user to monitor, analyze and control the connected equipment remotely.
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    Radar Characteristics Study for the Development of Surrogate Roadside Objects
    (2018) Lin, Jun; Chien, Stanley Yung-Ping; Christopher, Lauren; Chen, Yaobin
    Driving safety is a very important topic in vehicle development. One of the biggest threat of driving safety is road departure. Many vehicle active safety technologies have been developed to warn and mitigate road departure in recent years. In order to evaluate the performance of road departure warning and mitigation technologies, the standard testing environment need to be developed. The testing environment shall be standardized to provide consistent and repeatable features in various locations worldwide and in various seasons. The testing environment should also be safe to the vehicle under test in case the safety features do not function well. Therefore, soft, durable and reusable surrogates of roadside objects need to be used. Meanwhile, all surrogates should have the same representative characteristics of real roadside objects to di erent automotive sensors (e.g. radar, LIDAR and camera). This thesis describes the study on identifying the radar characteristics of common roadside objects, metal guardrail, grass, and concrete divider, and the development of the required radar characteristics of surrogate objects. The whole process is divided into two steps. The rst step is to nd the proper methods to measure the radar properties of those three roadside objects. The measurement result of each roadside object will be used as the requirement for making its surrogate. The second step is to create the material for developing the surrogate of each roadside object. In the experimental results demonstrate that all three surrogates satisfy their radar characteristics requirements.