190 research outputs found
A Sustainable Slashing Industry Using Biodegradable Sizes from Modified Soy Protein To Replace Petro-Based Poly(Vinyl Alcohol)
Biodegradable sizing agents from triethanolamine (TEA) modified soy protein could substitute poly(vinyl alcohol)(PVA) sizes for high-speed weaving of polyester and polyester/cotton yarns to substantially decrease environmental pollution and impel sustainability of textile industry. Nonbiodegradable PVA sizes are widely used and mainly contribute to high chemical oxygen demand (COD) in textile effluents. It has not been possible to effectively degrade, reuse or replace PVA sizes so far. Soy protein with good biodegradability showed potential as warp sizes in our previous studies. However, soy protein sizes lacked film flexibility and adhesion for required high-speed weaving. Additives with multiple hydroxyl groups, nonlinear molecule, and electric charge could physically modify secondary structure of soy protein and lead to about 23.6% and 43.3% improvement in size adhesion and ability of hair coverage comparing to unmodified soy protein. Industrial weaving results showed TEA-soy protein had relative weaving efficiency 3% and 10% higher than PVA and chemically modified starch sizes on polyester/cotton fabrics, and had relative weaving efficiency similar to PVA on polyester fabrics, although with 3− 6% lower add-on. In addition, TEA-soy sizes had a BOD5/COD ratio of 0.44, much higher than 0.03 for PVA, indicating that TEA-soy sizes were easily biodegradable in activated sludge
A Sustainable Slashing Industry Using Biodegradable Sizes from Modified Soy Protein To Replace Petro-Based Poly(Vinyl Alcohol)
Biodegradable sizing agents from triethanolamine (TEA) modified soy protein could substitute poly(vinyl alcohol)(PVA) sizes for high-speed weaving of polyester and polyester/cotton yarns to substantially decrease environmental pollution and impel sustainability of textile industry. Nonbiodegradable PVA sizes are widely used and mainly contribute to high chemical oxygen demand (COD) in textile effluents. It has not been possible to effectively degrade, reuse or replace PVA sizes so far. Soy protein with good biodegradability showed potential as warp sizes in our previous studies. However, soy protein sizes lacked film flexibility and adhesion for required high-speed weaving. Additives with multiple hydroxyl groups, nonlinear molecule, and electric charge could physically modify secondary structure of soy protein and lead to about 23.6% and 43.3% improvement in size adhesion and ability of hair coverage comparing to unmodified soy protein. Industrial weaving results showed TEA-soy protein had relative weaving efficiency 3% and 10% higher than PVA and chemically modified starch sizes on polyester/cotton fabrics, and had relative weaving efficiency similar to PVA on polyester fabrics, although with 3− 6% lower add-on. In addition, TEA-soy sizes had a BOD5/COD ratio of 0.44, much higher than 0.03 for PVA, indicating that TEA-soy sizes were easily biodegradable in activated sludge
A Survey of Forex and Stock Price Prediction Using Deep Learning
The prediction of stock and foreign exchange (Forex) had always been a hot
and profitable area of study. Deep learning application had proven to yields
better accuracy and return in the field of financial prediction and
forecasting. In this survey we selected papers from the DBLP database for
comparison and analysis. We classified papers according to different deep
learning methods, which included: Convolutional neural network (CNN), Long
Short-Term Memory (LSTM), Deep neural network (DNN), Recurrent Neural Network
(RNN), Reinforcement Learning, and other deep learning methods such as HAN,
NLP, and Wavenet. Furthermore, this paper reviewed the dataset, variable,
model, and results of each article. The survey presented the results through
the most used performance metrics: RMSE, MAPE, MAE, MSE, accuracy, Sharpe
ratio, and return rate. We identified that recent models that combined LSTM
with other methods, for example, DNN, are widely researched. Reinforcement
learning and other deep learning method yielded great returns and performances.
We conclude that in recent years the trend of using deep-learning based method
for financial modeling is exponentially rising
Signal Temporal Logic Control Synthesis among Uncontrollable Dynamic Agents with Conformal Prediction
The control of dynamical systems under temporal logic specifications among
uncontrollable dynamic agents is challenging due to the agents' a-priori
unknown behavior. Existing works have considered the problem where either all
agents are controllable, the agent models are deterministic and known, or no
safety guarantees are provided. We propose a predictive control synthesis
framework that guarantees, with high probability, the satisfaction of signal
temporal logic (STL) tasks that are defined over the system and uncontrollable
stochastic agents. We use trajectory predictors and conformal prediction to
construct probabilistic prediction regions for each uncontrollable agent that
are valid over multiple future time steps. Specifically, we reduce conservatism
and increase data efficiency compared to existing works by constructing a
normalized prediction region over all agents and time steps. We then formulate
a worst-case mixed integer program (MIP) that accounts for all agent
realizations within the prediction region to obtain control inputs that
provably guarantee task satisfaction with high probability. To efficiently
solve this MIP, we propose an equivalent MIP program based on KKT conditions of
the original one. We illustrate our control synthesis framework on two case
studies
EduSAT: A Pedagogical Tool for Theory and Applications of Boolean Satisfiability
Boolean Satisfiability (SAT) and Satisfiability Modulo Theories (SMT) are
widely used in automated verification, but there is a lack of interactive tools
designed for educational purposes in this field. To address this gap, we
present EduSAT, a pedagogical tool specifically developed to support learning
and understanding of SAT and SMT solving. EduSAT offers implementations of key
algorithms such as the Davis-Putnam-Logemann-Loveland (DPLL) algorithm and the
Reduced Order Binary Decision Diagram (ROBDD) for SAT solving. Additionally,
EduSAT provides solver abstractions for five NP-complete problems beyond SAT
and SMT. Users can benefit from EduSAT by experimenting, analyzing, and
validating their understanding of SAT and SMT solving techniques. Our tool is
accompanied by comprehensive documentation and tutorials, extensive testing,
and practical features such as a natural language interface and SAT and SMT
formula generators, which also serve as a valuable opportunity for learners to
deepen their understanding. Our evaluation of EduSAT demonstrates its high
accuracy, achieving 100% correctness across all the implemented SAT and SMT
solvers. We release EduSAT as a python package in .whl file, and the source can
be identified at https://github.com/zhaoy37/SAT_Solver
Fairguard: Harness Logic-based Fairness Rules in Smart Cities
Smart cities operate on computational predictive frameworks that collect,
aggregate, and utilize data from large-scale sensor networks. However, these
frameworks are prone to multiple sources of data and algorithmic bias, which
often lead to unfair prediction results. In this work, we first demonstrate
that bias persists at a micro-level both temporally and spatially by studying
real city data from Chattanooga, TN. To alleviate the issue of such bias, we
introduce Fairguard, a micro-level temporal logic-based approach for fair smart
city policy adjustment and generation in complex temporal-spatial domains. The
Fairguard framework consists of two phases: first, we develop a static
generator that is able to reduce data bias based on temporal logic conditions
by minimizing correlations between selected attributes. Then, to ensure
fairness in predictive algorithms, we design a dynamic component to regulate
prediction results and generate future fair predictions by harnessing logic
rules. Evaluations show that logic-enabled static Fairguard can effectively
reduce the biased correlations while dynamic Fairguard can guarantee fairness
on protected groups at run-time with minimal impact on overall performance.Comment: This paper was accepted by the 8th ACM/IEEE Conference on Internet of
Things Design and Implementatio
Robust Conformal Prediction for STL Runtime Verification under Distribution Shift
Cyber-physical systems (CPS) designed in simulators behave differently in the
real-world. Once they are deployed in the real-world, we would hence like to
predict system failures during runtime. We propose robust predictive runtime
verification (RPRV) algorithms under signal temporal logic (STL) tasks for
general stochastic CPS. The RPRV problem faces several challenges: (1) there
may not be sufficient data of the behavior of the deployed CPS, (2) predictive
models are based on a distribution over system trajectories encountered during
the design phase, i.e., there may be a distribution shift during deployment. To
address these challenges, we assume to know an upper bound on the statistical
distance (in terms of an f-divergence) between the distributions at deployment
and design time, and we utilize techniques based on robust conformal
prediction. Motivated by our results in [1], we construct an accurate and an
interpretable RPRV algorithm. We use a trajectory prediction model to estimate
the system behavior at runtime and robust conformal prediction to obtain
probabilistic guarantees by accounting for distribution shifts. We precisely
quantify the relationship between calibration data, desired confidence, and
permissible distribution shift. To the best of our knowledge, these are the
first statistically valid algorithms under distribution shift in this setting.
We empirically validate our algorithms on a Franka manipulator within the
NVIDIA Isaac sim environment
Rethinking Sensors Modeling: Hierarchical Information Enhanced Traffic Forecasting
With the acceleration of urbanization, traffic forecasting has become an
essential role in smart city construction. In the context of spatio-temporal
prediction, the key lies in how to model the dependencies of sensors. However,
existing works basically only consider the micro relationships between sensors,
where the sensors are treated equally, and their macroscopic dependencies are
neglected. In this paper, we argue to rethink the sensor's dependency modeling
from two hierarchies: regional and global perspectives. Particularly, we merge
original sensors with high intra-region correlation as a region node to
preserve the inter-region dependency. Then, we generate representative and
common spatio-temporal patterns as global nodes to reflect a global dependency
between sensors and provide auxiliary information for spatio-temporal
dependency learning. In pursuit of the generality and reality of node
representations, we incorporate a Meta GCN to calibrate the regional and global
nodes in the physical data space. Furthermore, we devise the cross-hierarchy
graph convolution to propagate information from different hierarchies. In a
nutshell, we propose a Hierarchical Information Enhanced Spatio-Temporal
prediction method, HIEST, to create and utilize the regional dependency and
common spatio-temporal patterns. Extensive experiments have verified the
leading performance of our HIEST against state-of-the-art baselines. We
publicize the code to ease reproducibility.Comment: 9 pages, accepted by CIKM'2
Reliability modeling and analysis of load-sharing systems with continuously degrading components
This paper presents a reliability modeling and analysis framework for load-sharing systems with identical components subject to continuous degradation. It is assumed that the components in the system suffer from degradation through an additive impact under increased workload caused by consecutive failures. A log-linear link function is used to describe the relationship between the degradation rate and load stress levels. By assuming that the component degradation is well modeled by a step-wise drifted Wiener process, we construct maximum likelihood estimates (MLEs) for unknown parameters and related reliability characteristics by combining analytical and numerical methods. Approximate initial guesses are proposed to lessen the computational burden in numerical estimation. The estimated distribution of MLE is given in the form of multivariate normal distribution with the aid of Fisher information. Alternative confidence intervals are provided by bootstrapping methods. A simulation study with various sample sizes and inspection intervals is presented to analyze the estimation accuracy. Finally, the proposed approach is illustrated by track degradation data from an application example
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