Nowadays, data-driven soft sensors have become mainstream for the key performance indicators prediction, which guarantees the safety and stability of the industrial process. The typical autoencoder ...(AE) has been widely used to extract potential features through unsupervised pretraining and supervised fine-tuning. However, most existing studies fail to consider both the time-varying features of the process and the differences in the contributions of the hidden features to the target variable. Therefore, in this article, a stacked spatial-temporal autoencoder (S 2 TAE) is proposed to enhance the representation learning capability for soft sensor modeling by taking the spatial-temporal correlations into consideration. Specifically, to effectively model the temporal dependence from nearby times, a temporal autoencoder is proposed, in which a memory module is devised and integrated to learn valuable historical information. Moreover, a "feature recalibration" block is developed and embedded into the spatial-temporal autoencoder (STAE) to selectively capture more informative features and suppress the less useful ones in a supervised way. Then, multiple STAEs are stacked to construct the S 2 TAE network to extract more robust high-level features. Finally, the experimental results on two real-world datasets of a sorbent decontamination system (SDS) desulfurization process and a high-low transformer demonstrate that the S 2 TAE-based soft sensor is effective and feasible.
Skeleton-based human action recognition has been a popular research field during the past few years. With the help of cameras equipping deep sensors, such as the Kinect, human action can be ...represented by a sequence of human skeleton data. Inspired by the skeleton descriptors based on Lie group, a spatial-temporal skeleton transformation descriptor (ST-STD) is proposed in this paper. The ST-STD describes the relative transformations of skeletons, including the rotation and translation during movement. It gives a comprehensive view of the skeleton in both spatial and temporal domain for each frame. To capture the temporal connections in the skeleton sequence, a denoising sparse long short term memory (DS-LSTM) network is proposed in this paper. The DS-LSTM is designed to deal with two problems in action recognition. First, to decrease the intra-class diversity, the spatial-temporal auto-encoder (STAE) is proposed in this paper to generate representations with higher abstractness. The denoising constraint and the sparsity constraint are applied on both spatial and temporal domain to enhance the robustness and to reduce action misalignment. Second, to model the action sequence, a three-layer LSTM structure is trained with STAE representations for temporal modeling and classification. The experiments are carried out on four popular datasets. The results show that our approach performs better than several existing skeleton-based action recognition methods, which prove the effectiveness of our method.
Fault diagnosis of dynamic multivariate systems is a challenging problem. In this article, a novel fault diagnosis scheme based on variable-wise stacked temporal autoencoder (VW-STAE) is proposed. ...First, a variable-wise strategy is proposed on the raw industrial data, which sorts the variables for a specific fault by its deviation factor and introduces fault label information during pretraining procedure. Then, temporal autoencoder (TAE) is designed to capture the temporal and spatial feature synchronously and model the complex dependencies of dynamic samples. The stacked TAE is built to enhance the ability of feature extraction by combining multiple TAEs. By inputting the sorted variables sequentially, the VW-STAE is trained as a binary classifier for a specific fault; thereby its input variables and the corresponding network parameters are ultimately selected according to the VW-STAE with the optimal diagnosis performance. Finally, a bank of VW-STAEs is adopted for all faults, which is followed by a fully connected layer to achieve comprehensive fault diagnosis result. The effectiveness of the proposed method is demonstrated in the sensorless drive diagnosis example. The results indicate that the proposed method outperforms other existing deep learning methods.
This paper presents an approach to diagnose batteries with the help of microcontroller and smartphone. It aims to determine the available capacity, the state of charge (SoC) and the state of health ...(SoH) of a battery. A battery is aged by charging and discharging cycles, this process degrades the chemical composition of the battery. Thus, this paper aims at using two-pulse test to determine Ampere hour capacity (AHC), SoC and SoH of a valve regulated lead acid (VRLA). These parameters are related to the voltage drop after each pulse of current discharge. By using a microcontroller and smartphone, it is possible to easily change the controller parameters according to different size of battery. The microcontroller will be used to control current discharge based on two-pulse method and a discrete PID. A smartphone with an application based on Android program and via Bluetooth communication allows to change the parameters for the controller and monitor the performance of battery test. This concept is validated by simulations and experiments on an emulated system for availability reasons. This work takes place in a larger process which aims at providing local solutions for local problems according to the "Jugaad" philosophy, which can be seen as "frugal innovation". The main idea is to re-use "old" elements such as automotive batteries in this case, to propose cheap and local solutions for off-grid village electrification.
Phasor measurement units can play a significant role in the development of a smart grid by making the power grid more aware of its operating states. Judiciously deployed PMUs in the power grid can ...convert the non-linear iterative state estimation into linear state measurement opening multitudes of possibilities for monitoring the power grid. A number of techniques/algorithms have been reported in the literature for determining the optimal number of PMUs to make a power grid observable. However communication between different PMUs installed in the grid as well as with the control center is a crucial aspect for implementing such system successfully. In this paper an algorithm which utilizes GIS for making better decision for PMU placement, ensuring communication feasibility, has been applied on a section of Indian power grid to determine the buses at which PMUs should be placed as well as the optimal location for placement of main transmitter/receiver antenna.
This article suggests that at national and local levels, the British state is seemingly incapable of solving multi-faceted and intractable social, economic and environmental problems alone. It is ...argued that new national and local governance arrangements, based on new ideas, different ways of working, and approaches to problem solving have brought into a sharper focus on the issues of democratic legitimacy, scrutiny and accountability. All three complex and ambiguous concepts have long been a concern in public administration. This article draws from existing conceptual frameworks to show that traditional forms of legitimacy, scrutiny and accountability are now under threat. It examines the merits of the new forms, with some recommendations for the future.
Despite such growing significance of public accountability, what is relatively missing from existing literature, however, is the study of such accountability in relation to the nature of the state. ...The modes, means, and degrees of accountability often vary among and within different state formations such as the capitalist state, the welfare state, the socialist state the bureaucratic state, and the developmental state. Adapted from the source document.
This article addresses the accountability practices in the Netherlands. It is argued that during the past few decades, nearly all constituting elements of classic accountability have been abandoned. ...The new practices involve audits, monitoring, performance measurement, evaluation, etc. It is argued that these practices have some serious side-effects, namely that accountability procedures have deteriorated in standard operations, resulting in an increase of administrative cost, insignificance, biased accounts, and ambiguous responsibility. The article concludes by a plea to reconsider the merits of modern managerial accountability.
