Case studies of thermally driven heat pump assisted drying

In general, most heat losses in industrial dryers arise due to the discharge of humid air. By using heat pump drying (HPD) systems, heat from the exhaust humid air can be recovered, thus improving the energy efficiency substantially. In this study, the performance of thermally driven HP integration in an animal food and a blood dryer were examined. Computer simulation models of the original high temperature dryers and the proposed system with HP integration and auxiliary heating were developed. It is found that, when using a gas engine, the maximum energy cost saving is limited by the temperature of the coolant fluid. The maximum energy cost saving when using a gas turbine is a bit higher, however at a much higher operating temperature

Improved Sensor Fault-Tolerant Control Technique Applied to Three-Phase Induction Motor Drive

An improved fault-tolerant control (FTC) method using mathematical functions is applied to the induction motor drive (IMD) against current sensors and speed encoder failures, which occur when the sensor is disconnected or completely damaged. The IMD with two current sensors and an encoder is speed controlled based on the field-oriented control (FOC) technique in regular operation. In this paper, an FTC unit is implemented in the FOC controller to detect and solve the sensor fault to increase the reliability of the speed control process. The measured stator currents and the feedback speed signal are integrated into the diagnosis algorithms to create a sensor fault-tolerant control function. Three diagnosis functions operating in a defined sequence are proposed for determining the health status of current and speed sensors. The FTC function performs isolation and replaces the faulty sensor signals with the proper estimated signals; then, the IMD will operate in the corresponding sensorless mode. Simulations will be performed to verify the accuracy and reliability of the proposed method under various sensor faults

Facile synthesis of 3D Fe2O3 nanostructures: sponge-like cube shape and bird nest-like architecture

The hierarchical nanostructures (3D) with their large specific surface area and abundant pores usually possess unique physical and chemical properties for various important applications. In this report, we have introduced simple and scalable routes to successfully synthesize 3D iron oxide nanostructures, including porous cubes and bird nest-like architecture. The 3D sponge-like Fe2O3 nanocubes were formed by an annealing process of perfect Prussian Blue (PB) microcubes, which were built from small nanoparticles linked together. Whereas, the 3D bird nest-like Fe2O3 nanostructures were formed by the transformation of C@FeOOH nanoflower precursors, which were constructed by primary nanorods. The results indicated that the obtained materials show monodispersity, uniform morphology, ultra-porosity and extremely high specific surface area. With unique characteristics, the 3D Fe2O3 nanostructures could be potential candidates for various important fields such as catalysts, absorption and gas sensors

HyperCUT: Video Sequence from a Single Blurry Image using Unsupervised Ordering

We consider the challenging task of training models for image-to-video deblurring, which aims to recover a sequence of sharp images corresponding to a given blurry image input. A critical issue disturbing the training of an image-to-video model is the ambiguity of the frame ordering since both the forward and backward sequences are plausible solutions. This paper proposes an effective self-supervised ordering scheme that allows training high-quality image-to-video deblurring models. Unlike previous methods that rely on order-invariant losses, we assign an explicit order for each video sequence, thus avoiding the order-ambiguity issue. Specifically, we map each video sequence to a vector in a latent high-dimensional space so that there exists a hyperplane such that for every video sequence, the vectors extracted from it and its reversed sequence are on different sides of the hyperplane. The side of the vectors will be used to define the order of the corresponding sequence. Last but not least, we propose a real-image dataset for the image-to-video deblurring problem that covers a variety of popular domains, including face, hand, and street. Extensive experimental results confirm the effectiveness of our method. Code and data are available at https://github.com/VinAIResearch/HyperCUT.gitComment: Accepted to CVPR 202

Driver Attention Tracking and Analysis

We propose a novel method to estimate a driver's points-of-gaze using a pair of ordinary cameras mounted on the windshield and dashboard of a car. This is a challenging problem due to the dynamics of traffic environments with 3D scenes of unknown depths. This problem is further complicated by the volatile distance between the driver and the camera system. To tackle these challenges, we develop a novel convolutional network that simultaneously analyzes the image of the scene and the image of the driver's face. This network has a camera calibration module that can compute an embedding vector that represents the spatial configuration between the driver and the camera system. This calibration module improves the overall network's performance, which can be jointly trained end to end. We also address the lack of annotated data for training and evaluation by introducing a large-scale driving dataset with point-of-gaze annotations. This is an in situ dataset of real driving sessions in an urban city, containing synchronized images of the driving scene as well as the face and gaze of the driver. Experiments on this dataset show that the proposed method outperforms various baseline methods, having the mean prediction error of 29.69 pixels, which is relatively small compared to the $1280{\times}720$ resolution of the scene camera

Adaptation options for agricultural cultivation systems in the South Central Coast under the context of climate change: Assessment Report.

This report highlights the results of consultation meetings and field visits organized by the Department of Crop Production and the CGIAR Research Program on Climate Change, Agriculture and Food Security in Southeast Asia in association with the three offices of the Department of Agriculture and Rural Development in the South Central Coast provinces of Binh Thuan, Ninh Thuan, and Khanh Hoa, in combination with consultation with the provinces in the conference: “Summing up crops production in the Winter-Spring season in 2018-2019, implementing the Summer-Autumn season, Main rice season in 2019 for the South Central Coast and the Central Highlands” held by the Ministry of Agriculture and Rural Development in Tam Ky City, Quang Nam Province on 12 April 2019. The meetings underlined the progress made by the provinces on climate change adaptation and mitigation, options for risk reductions in agricultural production, and conversion of crop structure as results of implementing the guidelines of the provinces and the Sector, especially, solutions for reservation and efficient and economic use of water under the context of climate change. This assessment report also reviews some issues related to the agricultural transformation of the region in adapting to risks caused by climate change. They are based on comparative advantages in terms of geographical location and market of key agricultural products. This report also points out shortcomings in using land and unreasonable points in managing and using important natural resources, especially water, and provides recommendations for the agricultural transformation and inter-regional connection with the Central Highlands and the Southeast. The team also introduces climate-related risks maps and adaptation plans (CS MAP) which is applied in the five provinces in the Mekong Delta Region, and hopes this solution’s expansion shall be supported by the Ministry of Agriculture and Rural Development and the provinces

A robust diagnosis method for speed sensor fault based on stator currents in the RFOC induction motor drive

A valid diagnosis method for the speed sensor failure (SSF) is an essential requirement to ensure the reliability of Fault-Tolerant Control (FTC) models in induction motor drive (IMD) systems. Most recent researches have focused on directly comparing the measured and estimated rotor speed signal to detect the speed sensor fault. However, using that such estimated value in both the fault diagnosis and the controller reconfiguration phases leads to the insufficient performance of FTC modes. In this paper, a novel diagnosis-technique based on the stator current model combined with a confusion prevention condition is proposed to detect the failure states of the speed sensor in the IMD systems. It helps the FTC mode to separate between the diagnosis and reconfiguration phases against a speed sensor fault. This proposed SSF diagnosis method can also effectively apply for IMs’ applications at the low-speed range where the speed sensor signal often suffers from noise. MATLAB/Simulink software has been used to implement the simulations in various speed ranges. The achieved results have demonstrated the capability and effectiveness of the proposed SSF method against speed sensor faults

An Improved Current-Sensorless Method for Induction Motor Drives Applying Hysteresis Current Controller

A novel strategy based on the feed-forward field-oriented control (FOC) method is proposed for the Hysteresis Current technique to control the induction motor (IM) drive without current sensors (CSs). A control scheme is proposed to estimate stator currents from reference rotor flux, rotor flux angle, and state variables as a replacement for the feedback-signal of CSs used in the hysteresis current controller (HCC). Here the rotor flux angle component is extracted from the feed-forward FOC loop. MATLAB/Simulink is applied to implement the simulations under many different operating conditions. The simulation results demonstrated the feasibility of the proposed method to obtain high performance in controlling the IM drives without the current sensors

A Framework for Assessing Environmental Incidents in Coastal Areas: A Case Study in the Southeastern Coastal Area of Vietnam

As developing dynamic regions, coastal areas have a high potential for environmental incidents, especially chemical spills, which may permanently threaten livelihoods and coastal ecosystems. Establishing an appropriate methodological framework for assessing environmental incidents in coastal areas, ensuring increased predictability and minimising potential consequences is a trend of interest to scientists. In this study, the environmental risk assessment model was applied to develop a framework for assessing environmental incidents in coastal areas due to chemical spills from the mainland based on hazard, exposure and vulnerability factors (i.e., sensitivity and adaptability). Using the multiple criteria decision-making (MCDM) method approach, suitable criteria, their optimal weights and the risk factors were determined. Modelling, remote sensing, and geographic information system (GIS) methods were used simultaneously for data collection, evaluation, and mapping. A case study was conducted in the coastal area of southeastern Vietnam, which comprises 27 subregions. These were classified into four environmental incident levels: low, medium, high, and extreme. Their prevalence was 70.37%, 3.70%, 7.41%, and 18.52% in the rainy season, and 74.07%, 7.41%, 7.41%, and 11.11% in the dry season, respectively. Based on analysis results and consultation with managers and experts, pertinent and practical solutions were proposed to reduce the risk of environmental incidents in subregions with high and extreme incident levels. Our results are expected to support policymakers in decision-making related to the sustainable development of the study area and complete the methodology framework for assessing environmental incidents in coastal areas due to multiple hazards