An application of metadata-based image retrieval system for facility management

For facility management, photography is an efficient and accurate method of recording the physical state of infrastructure. However, without an effective organizational scheme, the difficulty of retrieving relevant photos from historical databases can become overly burdensome for highly complex or long-lived assets. To make strategic decisions, it is crucial to retrieve the right information from a plurality of sources in a timely manner. The main objective of this paper is to present a method for organizing and retrieving photos from massive facility management photo databases using photo-metadata: photographed location, camera perspective, and image semantic content information. Indoor localization experiments were performed using Bluetooth technology to infer the location information. Perspective is inferred from the device’s on-board inertial measurement unit (IMU). Image semantic content is inferred using a Convolutional Neural Network (CNN)-based deep learning algorithm. Fusing these three features, seven query options were provided for the user when retrieving images. Leveraging Building Information Modeling (BIM) as a process and Geographic Information Systems (GIS) as a framework, this paper also envisions a federated information management by connecting 2D and 3D facility assets with our real-world map which can be smoothly bridged with our image retrieval system. The realization of the integrated application with BIM and GIS is significantly beneficial for the facility management domain by advancing the understanding of projects in a broader view with a federated data platform. In this research, the framework is illustrated with 21 institutional buildings within the University of Texas at Austin’s main campus, and the authors conclude that the proposed metadata-based image retrieval system can ultimately enhance the better-informed decision-making process through rapid information retrieval.     

STAR-Lite: A light-weight scalable self-taught learning framework for older adults’ activity recognition

Recognizing activities for older adults is challenging as we observe a variety of activity patterns caused due to aging (e.g., limited dexterity, limb control, slower response time) or/and underlying health conditions (e.g., dementia). However, existing literature with deep learning methods has successfully recognized activities when the dataset contains high-quality annotations and is captured in a controlled environment. On the contrary, data captured in a real-world environment, especially with older adults exhibiting memory-related symptoms, varying psychological and mental health status, reliance on caregivers to perform daily activities, and unavailability of domain-specific annotators, makes obtaining quality data with annotations challenging; leaving us with limited labeled data and abundant unlabeled data. In this paper, we hypothesize that projecting the labeled data representations comprising a specific set of activities onto a new representation space characterized by the unlabeled data comprising activities beyond the limited activities in the labeled dataset would help us rely less on the annotated data to improve activity detection performance. Motivated by this, we propose STAR-Lite, a self-taught learning framework that involves a pre-training framework to prepare the new representation space considering activities beyond the initial labels in the labeled dataset. STAR-Lite projects the labeled data representations on the new representation space characterized by unlabeled data labels and learns higher-level representations of the labeled dataset while optimizing inter- and intra- class distances without explicitly using a computation hungry similarity-based approach. We demonstrate that our proposed approach, STAR-Lite (a) improves activity recognition performance in a supervised setting and (b) is feasible for real-world deployment. To enhance the feasibility of deploying STAR-Lite on devices with limited memory resources, we explore model compression techniques such as pruning and quantization and propose a novel layer-wise pruning-rate optimization technique that effectively compresses the network while preserving the model performance. The evaluation was performed using the Alzheimer’s Activity Recognition dataset (AAR) captured from 25 individuals living in a retirement community center with IRB approval (#Y18NR12035) using an in-house SenseBox infrastructure while concurrently assessing the clinical evaluation of the participants for dementia, and independent living. Our extensive evaluation reveals that STAR-Lite can detect activities with an F1-score of 85.12% despite 62% reduction in model size and 5% improvement of execution time on a resource constrained device.     

Experimental evaluation of MPC-based anti-surge and process control for electric driven centrifugal gas compressors

The present work concerns model predictive control (MPC) of centrifugal gas compressors and describes the development of an MPC application for the tasks of anti-surge and process control. More specifically, the MPC formulation focuses on the question of how the transient manipulation of driver torque can be used to improve the performance of anti-surge and process control. For the purpose of testing and validating the proposed control algorithm, an experimental compressor test rig is presented, which is designed to mimic a typical centrifugal compressor application in the oil and gas industry. Modeling and parameter identification of the experimental setup is followed by the realization of the MPC solution on an embedded system to comply with the stringent real-time requirements for anti-surge control. Testing is performed with experiments using suction and discharge side disturbances, which are created by rapid valve closures. For comparison the same tests are repeated with conventional control approaches. The test results indicate improvements in maintaining the distance to surge by up to 11%, while at the same time reducing the process control settling time by up to 50%.     

Performance analysis of three advanced controllers for polymerization batch reactor: An experimental investigation

The performances of three advanced non-linear controllers are analyzed for the optimal set point tracking of styrene free radical polymerization (FRP) in batch reactors. The three controllers are the artificial neural network-based MPC (NN-MPC), the artificial fuzzy logic controller (FLC) as well as the generic model controller (GMC). A recently developed hybrid model (Hosen et al., 2011a. Asia-Pac. J. Chem. Eng. 6(2), 274) is utilized in the control study to design and tune the proposed controllers. The optimal minimum temperature profiles are determined using the Hamiltonian maximum principle. Different types of disturbances are introduced and applied to examine the stability of controller performance. The experimental studies revealed that the performance of the NN-MPC is superior to that of FLC and GMC.     

《微机化工应用》课程一人一不同卷考试改革教学实践

文章提出了《微机化工应用》课程考试中采用一人一不同卷的考试模式,介绍了一人一不同卷的试卷开发、考试组织、试卷答案求解及改卷工作中的具体情况,指出了该考试模式需要注意的一些具体问题。教学实践证明该考试模式对于实际应用型课程的考试是一种十分有效的考试方式,深受学生的欢迎。     

Integration of scheduling and control for batch processes using multi‐parametric model predictive control

Integration of scheduling and control results in Mixed Integer Nonlinear Programming (MINLP) which is computationally expensive. The online implementation of integrated scheduling and control requires repetitively solving the resulting MINLP at each time interval. (Zhuge and Ierapetritou, Ind Eng Chem Res. 2012;51:8550–8565) To address the online computation burden, we incorporare multi‐parametric Model Predictive Control (mp‐MPC) in the integration of scheduling and control. The proposed methodology involves the development of an integrated model using continuous‐time event‐point formulation for the scheduling level and the derived constraints from explicit MPC for the control level. Results of case studies of batch processes prove that the proposed approach guarantees efficient computation and thus facilitates the online implementation. © 2014 American Institute of Chemical Engineers AIChE J, 60: 3169–3183, 2014     

永磁同步电机优化模型预测双转矩控制

针对永磁同步电机模型预测转矩控制计算量大、权重系数难以确定和转矩脉动大的问题,提出优化模型预测双转矩控制方法。该方法用有功转矩和无功转矩双转矩代替磁链和转矩的代价函数,从而简化系统结构。构建具有预测-校正功能的补偿函数,对补偿函数进行校正,降低延时问题产生的影响。在此基础上,加入扇区判断环节,将待选电压从8个减为3个,大大减少计算量。仿真结果表明:优化模型预测双转矩控制方法转矩脉动更低、计算量更小、动态响应更快。     

基于NMPC-PID的大型风力机独立变桨控制

大型风力机的不平衡载荷会使叶片断裂,造成事故从而减短其使用寿命。针对大型风力发电系统在高风速区的不平衡载荷问题,提出一种NMPC-PID的独立变桨载荷控制方法。非线性模型预测控制(NMPC)用于及时调整风机桨叶的桨距角,PID控制则用于消除塔架振动的影响。运用机制方法在仿真平台搭建风力发电系统模型,运用所提控制策略进行仿真实验,并在两种情况下与模型预测控制方法(MPC)进行对比,验证了所提控制策略的可行性与有效性。     

Model test assessment of the generation of underground cavities and ground cave-ins by damaged sewer pipes

Ground cave-ins caused by damaged sewer pipes have been frequently reported in metropolitan areas and have caused severe socioeconomic losses. In this study, model experiments were conducted to determine how damaged sewer pipes generate ground cave-ins or cavities, with a focus on the influence of the soil type and density. Digital images of the model ground were captured to evaluate the internal deformation of the model grounds. Additionally, the vertical displacement at the surface, the size of the cavity, and the weight of the discharged soil were measured in each test. The results indicate that uniform soil with no fines content was more vulnerable to ground cavities than well-graded soil with fines content. Loose soil led to a ground cave-in with significant deformation over the entire model ground, whereas deformation was concentrated only on the ground cavities in the dense soil ground.     

国家公园管理模型的演变：从岛屿和网络到景观和社会生态方法

通过应用帕洛莫模型(Palomo's Model),回顾了新西兰国家公园和保护地管理方法的演变。结合新西兰林肯大学最近所进行的国家公园研究,对国家公园管理相关的岛屿方法、网络方法、景观方法和社会生态方法的关键特性进行分析,包括保护理念与实践,管理和规划维度,文化理念与价值观,以及每种方法的理论地位等。最后,指出景观方法和社会生态方法在当前国家公园和保护地管理中具有创新性价值,并与中国国家公园体制试点建设和生态文明发展方向高度契合。