An ensemble deep learning model for exhaust emissions prediction of heavy oil-fired boiler combustion

Accurate and reliable prediction of exhaust emissions is crucial for combustion optimization control and environmental protection. This study proposes a novel ensemble deep learning model for exhaust emissions (NOx and CO2) prediction. In this ensemble learning model, the stacked denoising autoencoder is established to extract the deep features of flame images. Four forecasting engines include artificial neural network, extreme learning machine, support vector machine and least squares support vector machine are employed for preliminary prediction of NOx and CO2 emissions based on the extracted image deep features. After that, these preliminary predictions are combined by Gaussian process regression in a nonlinear manner to achieve a final prediction of the emissions. The effectiveness of the proposed ensemble deep learning model is evaluated through 4.2Â MW heavy oil-fired boiler flame images. Experimental results suggest that the predictions are achieved from the four forecasting engines are inconsistent, however, an accurate prediction accuracy has been achieved through the proposed model. The proposed ensemble deep learning model not only provides accurate point prediction but also generates satisfactory confidence interval

Modal Parameter Identification Based on Hilbert Vibration Decomposition in Vibration Stability of Bridge Structures

Modal parameters are important parameters for the dynamic response analysis of structures. An output-only modal parameter identification technique based on Hilbert Vibration Decomposition (HVD) is developed herein for structural modal parameter identification to (1) obtain the Free Decay Response (FDR) of a structure through free vibration or ambient vibration tests, (2) decompose the FDR into modal responses using HVD, and (3) calculate the instantaneous frequencies and instantaneous damping ratios of the modal responses to obtain the modal frequencies and modal damping ratios. A series of numerical examples are examined to demonstrate the efficiency and highlight the superiorities of the proposed method relative to the empirical model decomposition-based (EMD-based) method. The robustness of the proposed method to noises is also investigated and proved to be positive effect. The proposed method is proved to be efficient in modal parameter identification for both linear and nonlinear systems, with better frequency resolution, and it can be applied to systems with closely spaced modes and low-energy mode

D2Taint: Differentiated and Dynamic Information Flow Tracking on Smartphones for Numerous Data Sources

Abstract—With smartphones ’ meteoric growth in recent years, leaking sensitive information from them has become an increasingly critical issue. Such sensitive information can originate from smartphones themselves (e.g., location information) or from many Internet sources (e.g., bank accounts, emails). While prior work has demonstrated information flow tracking’s (IFT’s) effectiveness at detecting information leakage from smartphones, it can only handle a limited number of sensitive information sources. This paper presents a novel IFT tagging strategy using differentiated and dynamic tagging. We partition information sources into differentiated classes and store them in fixed-length tags. We adjust tag structure based on time-varying received information sources. Our tagging strategy enables us to track at runtime numerous information sources in multiple classes and rapidly detect information leakage from any of these sources. We design and implement D2Taint, an IFT system using our tagging strategy on real-world smartphones. We experimentally evaluate D2Taint’s effectiveness with 84 real-world applications downloaded from Google Play. D2Taint reports that over 80 % of them leak data to third-party destinations; 14 % leak highly sensitive data. Our experimental evaluation using a standard benchmark tool illustrates D2Taint’s effectiveness at handling many information sources on smartphones with moderate runtime and space overhead

syncchecker: detecting synchronization errors between mpi applications and libraries

While improving the performance, nonblocking communication is prone to synchronization errors between MPI applications and the underlying MPI libraries. Such synchronization error occurs in the following way. After initiating nonblocking communication and performing overlapped computation, the MPI application reuses the message buffer before the MPI library completes the use of the same buffer, which may lead to sending out corrupted message data or reading undefined message data. This paper presents a new method called Sync Checker to detect synchronization errors in MPI nonblocking communication. To examine whether the use of message buffers is well synchronized between the MPI application and the MPI library, Sync Checker first tracks relevant memory accesses in the MPI application and corresponding message send/receive operations in the MPI library. Then it checks whether the correct execution order between the MPI application and the MPI library is enforced by the MPI completion check routines. If not, Sync Checker reports the error with diagnostic information. To reduce runtime overhead, we propose three dynamic optimizations. We have implemented a prototype of Sync Checker on Linux and evaluated it with seven bug cases, i.e., five introduced by the original developers and two injected, in four different MPI applications. Our experiments show that Sync Checker detects all the evaluated synchronization errors and provides helpful diagnostic information. Moreover, our experiments with seven NAS Parallel Benchmarks demonstrate that Sync Checker incurs moderate runtime overhead, 1.3-9.5 times with an average of 5.2 times, making it suitable for software testing. © 2012 IEEE.While improving the performance, nonblocking communication is prone to synchronization errors between MPI applications and the underlying MPI libraries. Such synchronization error occurs in the following way. After initiating nonblocking communication and performing overlapped computation, the MPI application reuses the message buffer before the MPI library completes the use of the same buffer, which may lead to sending out corrupted message data or reading undefined message data. This paper presents a new method called Sync Checker to detect synchronization errors in MPI nonblocking communication. To examine whether the use of message buffers is well synchronized between the MPI application and the MPI library, Sync Checker first tracks relevant memory accesses in the MPI application and corresponding message send/receive operations in the MPI library. Then it checks whether the correct execution order between the MPI application and the MPI library is enforced by the MPI completion check routines. If not, Sync Checker reports the error with diagnostic information. To reduce runtime overhead, we propose three dynamic optimizations. We have implemented a prototype of Sync Checker on Linux and evaluated it with seven bug cases, i.e., five introduced by the original developers and two injected, in four different MPI applications. Our experiments show that Sync Checker detects all the evaluated synchronization errors and provides helpful diagnostic information. Moreover, our experiments with seven NAS Parallel Benchmarks demonstrate that Sync Checker incurs moderate runtime overhead, 1.3-9.5 times with an average of 5.2 times, making it suitable for software testing. © 2012 IEEE

Combustion stability monitoring through flame imaging and stacked sparse autoencoder based deep neural network

Combustion instability is a well-known problem in the combustion processes and closely linked to lower combustion efficiency and higher pollutant emissions. Therefore, it is important to monitor combustion stability for optimizing efficiency and maintaining furnace safety. However, it is difficult to establish a robust monitoring model with high precision through traditional data-driven methods, where prior knowledge of labeled data is required. This study proposes a novel approach for combustion stability monitoring through stacked sparse autoencoder based deep neural network. The proposed stacked sparse autoencoder is firstly utilized to extract flame representative features from the unlabeled images, and an improved loss function is used to enhance the training efficiency. The extracted features are then used to identify the classification label and stability index through clustering and statistical analysis. Classification and regression models incorporating the stacked sparse autoencoder are established for the qualitative and quantitative characterization of combustion stability. Experiments were carried out on a gas combustor to establish and evaluate the proposed models. It has been found that the classification model provides an F1-score of 0.99, whilst the R-squared of 0.98 is achieved through the regression model. Results obtained from the experiments demonstrated that the stacked sparse autoencoder model is capable of extracting flame representative features automatically without having manual interference. The results also show that the proposed model provides a higher prediction accuracy in comparison to the traditional data-driven methods and also demonstrates as a promising tool for monitoring the combustion stability accurately

Preparation of ITO Nanoparticles by Liquid Phase Coprecipitation Method

The nanoscale indium tin oxide (ITO) particles are synthesied by liquid phase coprecipitation method under given conditions with solution of indium chloride, tin chloride, and ammonia. The absolute ethyl alcohol or deionized water was used as solvent and the dodecylamine or hexadecylamine surfactant was used as a dispersant in the reaction system. The sample powder was characterized by X-ray diffraction (XRD), transmission electron microscopy (TEM), and high-resolution electron microscopy (HRTEM). Based on the transmission electron micrograph, the influences of the two different solvents and the two different dispersants on the nanoparticle size and dispersion were studied, respectively. The results showed that the ITO particles are finely crystallized body-centered cubic structure. The particle size has distributed in 30 nm to 90 nm

SyncChecker: Detecting Synchronization Errors between MPI Applications and Libraries

Abstract—While improving the performance, nonblocking communication is prone to synchronization errors between MPI applications and the underlying MPI libraries. Such synchronization error occurs in the following way. After initiating nonblocking communication and performing overlapped computation, the MPI application reuses the message buffer before the MPI library completes the use of the same buffer, which may lead to sending out corrupted message data or reading undefined message data. This paper presents a new method called SyncChecker to detect synchronization errors in MPI nonblocking communication. To examine whether the use of message buffers is well synchronized between the MPI application and the MPI library, SyncChecker first tracks relevant memory accesses in the MPI application and corresponding message send/receive operations in the MPI library. Then it checks whether the correct execution order between the MPI application and the MPI library is enforcedbytheMPIcompletioncheckroutines.Ifnot,SyncChecker reports the error with diagnostic information. To reduce runtime overhead, we propose three dynamic optimizations. We have implemented a prototype of SyncChecker on Linux and evaluated it with seven bug cases, i.e., five introduced by the original developers and two injected, in four different MPI applications. Our experiments show that SyncChecker detects all the evaluated synchronization errors and provides helpful diagnostic information. Moreover, our experiments with seven NAS Parallel Benchmarks demonstrate that SyncChecker incurs moderate runtime overhead, 1.3-9.5 times with an average of 5.2 times, making it suitable for software testing

A hybrid deep neural network based prediction of 300 MW coal-fired boiler combustion operation condition

In power generation industries, boilers are required to be operated under a range of different conditions toaccommodate demands for fuel randomness and energy fluctuation. Reliable prediction of the combustionoperation condition is crucial for an in-depth understanding of boiler performance and maintaining high combustionefficiency. However, it is difficult to establish an accurate prediction model based on traditional data-driven methods,which requires prior expert knowledge and a large number of labeled data. To overcome these limitations, a novelprediction method for the combustion operation condition based on flame imaging and hybrid deep neural networkis proposed. The proposed hybrid model is a combination of convolutional sparse autoencoder (CSAE) and leastsupport vector machine (LSSVM), i.e., CSAE-LSSVM, where the convolutional sparse autoencoder with deeparchitectures is utilized to extract the essential features of flame image, and then essential features are input intothe least support vector machine for operation condition prediction. A comprehensive investigation of optimalhyper-parameter and dropout technique is carried out to improve the performance of the CSAE-LSSVM. Theeffectiveness of the proposed model is evaluated by 300MW tangential coal-fired boiler flame images. Theprediction accuracy of the proposed hybrid model reaches 98.06%, and its prediction time is 3.06millisecond/image. It is observed that the proposed model could present a superior performance in comparison toother existing neural network models