Mismatch Happens Research Articles

Assessment of air pollution and air quality perception mismatch using mobility-based real-time exposure.

Air pollution poses a threat to human health. Public perceptions of air pollution are important for individual self-protection and policy-making. Given the uncertainty faced by residence-based exposure (RB) measurements, this study measures individuals' real-time mobility-based (MB) exposures and perceptions of air pollution by considering people's daily movement. It explores how contextual uncertainties may influence the disparities in perceived air quality by taking into account RB and MB environmental factors. In addition, we explore factors that are related to the mismatch between people's perceived air quality and actual air pollution exposure. Using K-means clustering to divide the PM2.5 values into two groups, a mismatch happens when the perceived air quality is poor but the air pollution level is lower than 15.536μg/m3 and when the perceived air quality is good but the air pollution level is higher than 15.608μg/m3. The results show that there is a mismatch between air pollution exposure and perception of air pollution. People with low income are exposed to higher air pollution. Unemployed people and people with more serious mental health symptoms (e.g., depression) have a higher chance of accurately assessing air pollution (e.g., perceiving air quality as poor when air pollution levels are high). Older people and those with a higher MB open space density tend to underestimate air pollution. Students tend to perceive air quality as good. People who are surrounded by higher MB transportation land-use density and green space density tend to perceive air quality as poor. The results can help policymakers to increase public awareness of high air pollution areas, and consider the health effects of landscapes during planning.

An improved dq-frame wideband impedance identification method for power supply network with only one disturbance

To cope with the energy crisis and environmental degradation, the new energy represented by wind and photovoltaic power generations have developed rapidly, which leads to a large number of power electronic converters being connected to the power grid. In this context, the power-electronic-based power system has become a trend. However, when wideband impedance mismatch happens between the power electronic converter and the connected power supply network, the wideband oscillation will occur in the system, which seriously endangers the safe and stable operation of the power system. To solve it, impedance identification is a key step to obtain the dq-frame impedance characteristics to analyze the power system stability. This article focuses on the power supply network and proposes an improved method to identify its dq-frame wide-band impedance by only one disturbance, which greatly simplifies the dq-frame wideband impedance identification process to improve the identification efficiency. It is of great significance for the safe and stable operation of the power-electronic-based power system.

Spatio-Temporal association rule based deep annotation-free clustering (STAR-DAC) for unsupervised person re-identification

Multi-camera video surveillance environment has a variety of emerging research problems among, which person re-identification is the premier one. Unsupervised person re-identification has been explored less in literature than the supervised approach. Images acquired from the video surveillance systems are unlabeled, which denotes that it is naturally an unsupervised learning problem. The state-of-the-art unsupervised methods seek external annotations support such as incorporating transfer learning techniques, partial labeling of train images, etc., which makes them not purely unsupervised and unsuitable for practical real-world surveillance settings. Identity mismatch happens due to the similar costumes and complex environmental factors. To resolve this issue, we introduce a new framework named Spatio-Temporal Association Rule based Deep Annotation-free Clustering (STAR-DAC) which incrementally clusters the unlabeled person re-identification images based on visual features and performs cluster fine-tuning through the mined spatio-temporal association rules. STAR formulations leveraged upto 75% of images for reliable sample selection through cluster fine-tuning. STAR based fine-tune algorithm aims to attain ground-truth labels of an unlabeled dataset and eliminate cluster outliers to stabilize the evaluation. Experiments are performed on image and video-based benchmark person re-identification datasets such as DukeMTMC re-ID, Market1501, MSMT17, CUHK03, GRID and Dukevideo re-ID, iLIDSVid, ViPer respectively. Experimental results clearly show that the proposed STAR-DAC framework outperforms the state-of-the-art methods in case of large scale datasets with multiple cameras.

Power Quality Enhancement Using Dynamic Voltage Restorer (DVR)-Based Predictive Space Vector Transformation (PSVT) With Proportional Resonant (PR)-Controller

In the power distribution system, the Power Quality (PQ) is disturbed by the voltage sag and swells. The Dynamic Voltage Restorer (DVR) is used to enhance various PQ problems like voltage sag, swells, and Harmonics. The previous Intrinsic Space Vector Transformation (ISVT) control techniques with DVR system, to compensate the power quality issue. It produced a steady-state error, low efficiency, and high THD. The SMES based DVR has provided excellent results in overcoming these issues. The energy is stored by DVR through a storage element, which takes alternate energy from a Solar PV cell. The Maximum Power Point Tracking (MPPT) based P&O algorithm is implemented to equalize the solar power. The Voltage Source Inverter (VSI) generates the reactive power, which has to be compensated with the help of Pulse Width Modulation (PWM) and the feedback control loop are essential to enhance the injection of reactive power to the line. Due to this reason, a proposed Predictive Space Vector Transformation (PSVT) control-based DVR is implemented. It analyzes the variation of power on the distribution side and generates the proper feedback control to the inverter systems. In the instant of voltage injection to the line with the help of DVR, the phase angle mismatch happens which is not synchronized reactive power to the grid. Due to the non-synchronization of reactive power, more harmonic distortion is generated. A Proportional Resonant (PR) controller is introduced, which is Proportional Resonant (PR) current controller. The current injected by the inverter into the grid in phase with the grid voltage maintain constant with unity power factor. The PR controller design is cascaded with a harmonic compensator to mitigate low order odd harmonic components present in the output current of VSI and minimize the total harmonic distortion (THD). The performance of the proposed is evaluated using MATLAB 2017b software.

First-principles investigation on the structural, electronic, vibrational and magnetic properties of the Co-substituted orthorhombic SrSnO3

Half-metallic (HM) ferromagnets provide an excellent possibility to design spintronic applications, yet a limited number of ferromagnetic materials are available which provide half-metallicity only in one spin channel. Moreover, often theoretically explored HM gap disappears when the material is synthesized experimentally due to small lattice mismatch happens which further hinders the advancement of spin-based applications. In this study, we have systematically explored the structural, electronic, formation, vibrational and magnetic properties of the Co-substituted SrSn(1−x)CoxO3wherex=0,0.25,0.5,0.75 (SSCO) alloys by employing first-principles methods with hybrid functionals. The Co-doping at the Sn-site induced the spontaneous magnetism into the non-magnetic orthorhombic SrSnO3 (SSO) perovskite material. Our calculations show that there are two ferromagnetic semiconductor materials found at the Co doping concentration of x = 0.5 & 0.75 with the large values of energy bandgaps. Furthermore, HM ferromagnetic ground state appears for the Co-doping of SSO at x = 0.25 which is vibrationally stable at the Г-point and has an integral magnetic moment of 1μB. The orthorhombic SrSn0.75Co0.25O3 alloy can be the prospective contender for the spintronic applications due to its thermodynamic and dynamic stability and large value of the HM gap (0.38 eV).

Multichannel signal detection in interference and noise when signal mismatch happens

In this paper, we consider the problem of detecting a multichannel signal in interference and noise when signal mismatch happens. We first propose two selective detectors, since their strong selectivity is preferred in some situations. However, these two detectors would not be suitable candidates if a robust detector is needed. To overcome this shortcoming, we then devise a tunable detector, which is parametrized by a non-negative scaling factor, referred to as the tunable parameter. By adjusting the tunable parameter, the proposed detector can smoothly change its capability in rejecting or robustly detecting a mismatch signal. Moreover, one selective detector and the tunable detector with an appropriate tunable parameter can provide nearly the same detection performance as existing detectors in the absence of signal mismatch. We obtain analytical expressions for the probabilities of detection (PDs) and probabilities of false alarm (PFAs) of the three proposed detectors, which are verified by Monte Carlo simulations.

A reason why visual-functional mismatch happens: Insights from mathematical models

A reason why visual-functional mismatch happens: Insights from mathematical models

Mismatch of Talent: Evidence on Match Quality, Entry Wages, and Job Mobility

We examine the direct impact of idiosyncratic match quality on entry wages and job mobility using unique data on worker talents matched to job-indicators and individual wages. Tenured workers are clustered in jobs with high job-specific returns to their types of talents. We therefore measure mismatch by how well the types of talents of recent hires correspond to the talents of tenured workers performing the same jobs. A stylized model shows that match quality has a smaller impact on entry wages but a larger impact on separations and future wage growth if matches are formed under limited information. Empirically, we find such patterns for inexperienced workers and workers who were hired from non-employment, which are also groups where mismatch is more pronounced on average. Most learning about job-specific mismatch happens within a year. Experienced job-to-job movers appear to match under much less uncertainty. They are better matched on entry and mismatch have a smaller effect on their initial separation rates and later wage growth. Instead, match quality is priced into their starting wages.

Modeling and solving term mismatch for full-text retrieval

Even though modern retrieval systems typically use a multitude of features to rank documents, the backbone for search ranking is usually the standard tf.idf retrieval models. This thesis addresses a limitation of the fundamental retrieval models, the term mismatch problem, which happens when query terms fail to appear in the documents that are relevant to the query. The term mismatch problem is a long standing problem in information retrieval. However, it was not well understood how often term mismatch happens in retrieval, how important it is for retrieval, or how it affects retrieval performance. This thesis answers the above questions, and proposes principled solutions to address this limitation. The new understandings of the retrieval models will benefit its users, as well as inform the development of software applications built on top of them. This new direction of research is enabled by the formal definition of the probability of term mismatch, and quantitative data analyses around it. In this thesis, term mismatch is defined as the probability of a term not appearing in a document that is relevant to the query. The complement of term mismatch is the term recall, the probability of a term appearing in relevant documents. Even though the term recall probability is known to be a fundamental quantity in the theory of probabilistic information retrieval, prior research in ad hoc retrieval provided few clues about how to estimate term recall reliably. This dissertation research designs two term mismatch prediction methods. With exploratory data analyses, this research first identifies common reasons that user-specified query terms fail to appear in documents relevant to the query, develops features correlated with each reason, and integrates them into a predictive model that can be trained from data. This prediction model uses training queries with relevance judgments to predict term mismatch for test queries without known relevance, and can be viewed as a type of transfer learning where training queries represent related ranking tasks that are used by the learning algorithm to facilitate the ranking for new test tasks. Further data analyses focus on the variation of the term mismatch probability for the same term across different queries, and demonstrate that query dependent features are needed for effective term mismatch prediction. At the same time, because the cross-query variation of term mismatch is small for most of the repeating term occurrences, a second mismatch prediction method is designed to use historic occurrences of the same term to predict the mismatch probability for its test occurrences. This provides an alternative and more efficient procedure to predict term mismatch. Effective term mismatch predictions can be used in several different ways to improve retrieval. The probabilistic retrieval theory suggests to use the term recall probabilities as term weights in the retrieval models. Experiments on 6 different TREC Ad hoc track and Web track datasets show that this automatic intervention improves both retrieval recall and precision substantially for long queries. Even though term weighting does not substantially improve retrieval accuracy for short queries which typically have a higher baseline performance, much larger gains are possible by solving mismatch using user expanded Conjunctive Normal Form queries. These queries try to fix the mismatch problem by expanding every query term individually. Our method uses the automatic term mismatch predictions as a diagnostic tool to guide interactive interventions, so that the users can expand the query terms that need expansion most. Simulated expansion interactions based on real user-expanded queries on TREC Ad hoc and Legal track datasets show that expanding the terms that have the highest predicted mismatch probabilities effectively improves retrieval performance. The resulting Boolean Conjunctive Normal Form expansion queries are both compact and effective, substantially outperforming the short keyword queries as well as the traditional bag of

Prediction of the Melt Index Based on the Relevance Vector Machine with Modified Particle Swarm Optimization

AbstractA novel chemical soft‐sensor approach for the prediction of the melt index (MI) in the propylene polymerization industry is presented. The MI is considered as one of the important variables of quality that determine the product specifications. Thus, a reliable estimation of the MI is crucial in quality control. An accurate optimal predictive model of MI values with the relevance vector machine (RVM) is proposed, where the RVM is employed to build the MI prediction model; a modified particle swarm optimization (MPSO) algorithm is then introduced to optimize the parameter of the RVM, and the MPSO‐RVM model is thereby developed. An online correcting strategy (OCS) is further carried out to update the modeling data and to revise the model's parameter self‐adaptively whenever model mismatch happens. Based on the data from a real polypropylene production plant, a detailed comparison is carried out among the least squares support vector machine (LS‐SVM), RVM, MPSO‐RVM, and OCS‐MPSO‐RVM models. The research results reveal the prediction accuracy and validity of the proposed approach.

Modeling of Pro-supination for Forearm Skeleton Based on MRI

The continuous passive motion (CPM) device we developed for the elbow joint implements pro-supination of the forearm naturally accompanying flexion and extension of the elbow joint. As the method of making the curative effect due to accompanying pro-supination clear, the range of motion (ROM) of the forearm skeleton are estimated by using of the pro-supination model of the forearm skeleton. As the pro-supination model of the forearm skeleton, there are the models Fick and others and Kecskemethy and others proposed. But, hypothesis is set in regard to the link length of the model and the rotation axis of the forearm. So, in the case of pro-supination of the forearm, it is thought that mismatch happens between the forearm skeletal model and the forearm skeleton. In this paper, by analyzing magnetic resonance imaging (MRI) of the forearm, we proposed the newer forearm skeletal model based on the biomechanics.

Perception of self-generated action in schizophrenia

Introduction. Self-generated actions involve central processes of sensorimotor integration that continuously monitor sensory inputs to ensure that motor outputs are congruent with our intentions. This mechanism works automatically in normal conditions but becomes conscious whenever a mismatch happens during the execution of action between expected and current sensorimotor reafferences. It is now admitted in the literature that sensorimotor processes as well as the ability to predict the consequences of our own actions imply the existence of a forward model of action, which is based on efference copies. Recently, it has been proposed that positive symptoms expressed by schizophrenic patients, such as delusions of control or thought insertions, arise because of a deficiency in this forward model, and more particularly, because of a lack of awareness of certain aspects of motor control derived from such an internal model. Method. To test further this hypothesis, 19 schizophrenic patients (10 with and 9 without Schneiderian symptoms) and 19 control subjects performed a visuo-motor conflict task and had verbally to report the felt position of their hand at the end of each trial. Results. Under this experimental procedure, schizophrenic patients - whatever their clinical phenotype - failed to switch to a conscious representation of their hand movements, and then consequently to maintain their level of performance for the sensorimotor adjustment in comparison with controls. Conclusion. Our findings suggest two facts. First, that a functional monitoring of action, based on a forward

Extending standard UML with model composition semantics

There is a well documented problem in the software engineering field relating to a structural mismatch between the specification of requirements for software systems and the specification of object-oriented software systems. The structural mismatch happens because the units of interest during the requirements phase (for example, feature, service, capability, function, etc.) are different to the units of interest during object-oriented design and implementation (for example, object, class, method, etc.). The structural mismatch results in support for a single requirement being scattered across the design units and a single design unit supporting multiple requirements—this in turn results in reduced comprehensibility, traceability and reuse of design models. This paper presents an approach to designing systems based on the object-oriented model, but extending this model by adding new decomposition capabilities. The new decomposition capabilities support a way of directly aligning design models with individual requirements. Each model contains a design of an individual requirement, with concepts from the domain (which may appear in multiple requirements) designed from the perspective of that requirement. Standard UML is used to design the models decomposed in this way. Composition of design models is supported, and it is specified with a composition relationship. A composition relationship specifies how models are to be composed by identifying overlapping concepts in different models and specifying how models should be integrated. This paper describes changes required to the UML metamodel to support composition relationships. Two kinds of integration strategies are discussed— merge and override.