Stack Module Research Articles

A novel multilevel stacked SqueezeNet model for handwritten Chinese character recognition

To solve the problems of large number of similar Chinese characters, difficult feature extraction and inaccurate recognition, we propose a novel multilevel stacked SqueezeNet model for handwritten Chinese character recognition. First, we design a deep convolutional neural network model for feature grouping extraction and fusion. The multilevel stacked feature group extraction module is used to extract the deep abstract feature information of the image and carry out the fusion between the different feature information modules. Secondly, we use the designed down-sampling and channel amplification modules to reduce the feature dimension while preserving the important information of the image. The feature information is refined and condensed to solve the overlapping and redundant problem of feature information. Thirdly, inter-layer feature fusion algorithm and Softmax classification function constrained by L2 norm are used. We further compress the parameter clipping to avoid the loss of too much accuracy due to the clipping of important parameters. The dynamic network surgery algorithm is used to ensure that the important parameters of the error deletion are reassembled. Experimental results on public data show that the designed recognition model in this paper can effectively improve the recognition rate of handwritten Chinese characters.

R&D status for an innovative crystal calorimeter for the future Muon Collider

The measurement of physics processes at new energy frontier experiments requires excellent spatial, time, and energy resolutions to resolve the structure of collimated high-energy jets. Calorimeters, as other detectors, must face this increasing performance demand. In a future TeV-scale Muon Collider, the beam-induced background (BIB) represents the main challenge in the design of the detectors and of the event reconstruction algorithms and can pose serious limitations to the physics performance. However, it is possible to reduce the BIB impact on the Muon Collider calorimeter by exploiting some of its characteristics and by ensuring high granularity, excellent timing, longitudinal segmentation and good energy resolution. The proposed R&D is an innovative semi-homogeneous electromagnetic calorimeter based on stackable modules of lead fluoride crystals (PbF2) readout by surface-mount UV-extended Silicon Photomultipliers (SiPMs): the Crilin calorimeter (CRystal calorImeter with Longitudinal INformation). The calorimeter should operate in a very harsh radiation environment, withstanding yearly a neutron flux of 1014 n1MeV /cm2 and a dose of 100 krad. In this paper, the radiation tolerance measured in several irradiation campaigns and the timing performances evaluated during a test beam at CERN-H2 with 120-GeV electron are discussed. A description of the latest prototype Proto-1, that will be shortly tested, is also provided.

Hydrodynamic Response Analysis of Hybrid Floating Structure according to Length of Damping Plate

New businesses such as marine leisure and floating construction have recently flourished on the coast. As a result, consideration is given to a floating structure appropriate for marine environments. In this study, we applied a damping plate to increase the stability of a hybrid floating structure that was expanded by vertical and horizontal stacking of mobile unit modules. In the numerical analysis using ANSYS AQWA, the behavioral characteristics of the floating structure were analyzed according to the length change of the damping plate. However, the wave forces acting on a floating structure are excessively calculated by the resonance of fluid surrounded by the structure in the numerical analysis using potential flow. Therefore, we used the damping zone option of the ANSYS AQWA in the frequency domain analysis.

1 kV, 10-kW SiC-Based Quadruple Active Bridge DCX Stage in a DC to Three-Phase AC Module for Medium-Voltage Grid Integration

Interfacing low voltage dc to medium voltage three-phase ac grid is often based on series-stackable modular converter architectures. To minimize energy storage requirements, it is advantageous to employ a quadruple active bridge (QAB) stage operating as a “dc transformer” in each stackable module. The QAB stage offers three isolated dc link voltages, which then allow for flexible stacking of three single-phase dc-to-ac inverter stages. Each of the module phases processes a pulsating power having a component at twice the line frequency. This presents a challenge in maintaining zero voltage switching (ZVS) on the secondary sides of the QAB during low-power portions of the line cycle. This article is focused on the design of the QAB stage. A detailed analysis of ZVS switching waveforms is presented, including effects of nonlinear device capacitances. It is shown how ZVS can be achieved at all times using a relatively small circulating current provided by the magnetizing inductance of the high-frequency transformer. Analytical expressions are given for the optimal values of the magnetizing inductance and the dead times of the QAB primary and secondary bridges. The approach is verified by experimental results on a 1 kV, 10-kW SiC-based prototype, demonstrating a relatively flat efficiency curve with a peak efficiency of 97.1% at 75% load.

(Invited) Automated Production of High Performance PEMFC Stacks and Components According to Automotive Requirements

EKPO Fuel Cell GmbH was founded in 2021 by two parent companies, ElringKlinger and Plastic Omnium, whom both are strong and established automotive suppliers with mass manufacturing capabilities and strategic approaches to hydrogen and the fuel cell businesses. The aim of EKPO is the development and production of proton exchange membrane fuel cell stack components and stack modules. Drawing from the experiences of the pilot automated production line since 2016, a fully optimized automated production, with improved production technologies and products, was developed by EKPO in 2021 to achieve an output capability of 10,000 stacks per year. The line was designed with a modular and scalable approach that allows flexibility as well as increased capacity by parallelization. The implementation is successful of producing EKPO’s various stack platforms while maintaining a high level of quality and production standard for the automotive industry.Since EKPO offers a wide range of products, the automated line can cover stack power ranges from 10 kW up to > 200 kW. As a prerequisite for excellent technical performance, in addition to function quality measures were set as key targets from the start of the development. EKPO has thus established consistent and stringent quality process flows and processes for all production steps. These measures include the early involvement of measurability and manufacturability assessment on all process and analysis steps on the different levels (i.e., components and stack). The feedback from each workflow can then continuously improve products, processes, and documentation regarding product specific codes standards and regulations (CSR).The accumulative knowledge from two decades of activity of fuel cell component and stack development enabled EKPO to establish a production range starting from cell and stack components (i.e., bipolar plates, sealings, and plastic end plate modules) to fuel cell stacks and modules produced by fully automated MEA and stack assembly and end-of-line testing. The automated production covers all processes from bipolar plate manufacturing and assembly, CCM and GDL roll good cutting, MEA-assembly, stacking, and the final compression and characterization of the fuel cell stack. The quality matrixes include inspecting performance key figures as well as quality and geometries of the stack and its components. The incoming materials are typically supplied as roll goods, (i.e., GDL, CCM, and metal coil) or as raw material (i.e., plastic granules). As the incoming materials and finished components are very sensitive to environmental conditions and particle contamination, the entire MEA processing and stack assembly process takes place in a cleanroom with controlled climate for maximum process stability and reliability.With a key approach of “design to manufacture” and deep understanding of stack technology and component requirements, EKPO is able to deliver two best in class stacks, namely the NM5 evo and NM12 single and twin platforms.

NetKernel: Making Network Stack Part of the Virtualized Infrastructure

This paper presents a system called NetKernel that decouples the network stack from the guest virtual machine and offers it as an independent module. NetKernel represents a new paradigm where network stack can be managed as part of the virtualized infrastructure. It provides important efficiency benefits: By gaining control and visibility of the network stack, operators can perform network management more directly and flexibly, such as multiplexing VMs running different applications to the same network stack module to save CPU cores, and enforcing fair bandwidth sharing. Users also benefit from the simplified stack deployment and better performance: For example mTCP can be deployed without API change to support nginx natively, and shared memory networking can be readily enabled to improve performance of colocated VMs. Testbed evaluation using 100G NICs shows that NetKernel preserves the performance and scalability of both kernel and userspace network stacks, and provides the same isolation as the current architecture.

An effective modular approach for crowd counting in an image using convolutional neural networks

Abrupt and continuous nature of scale variation in a crowded scene is a challenging task to enhance crowd counting accuracy in an image. Existing crowd counting techniques generally used multi-column or single-column dilated convolution to tackle scale variation due to perspective distortion. However, due to multi-column nature, they obtain identical features, whereas, the standard dilated convolution (SDC) with expanded receptive field size has sparse pixel sampling rate. Due to sparse nature of SDC, it is highly challenging to obtain relevant contextual information. Further, features at multiple scale are not extracted despite some inception-based model is not used (which is cost effective). To mitigate theses drawbacks in SDC, we therefore, propose a hierarchical dense dilated deep pyramid feature extraction through convolution neural network (CNN) for single image crowd counting (HDPF). It comprises of three modules: general feature extraction module (GFEM), deep pyramid feature extraction module (PFEM) and fusion module (FM). The GFEM is responsible to obtain task independent general features. Whereas, PFEM plays a vital role to obtain the relevant contextual information due to dense pixel sampling rate caused by densely connected dense stacked dilated convolutional modules (DSDCs). Further, due to dense connections among DSDCs, the final feature map acquires multi-scale information with expanded receptive field as compared to SDC. Due to dense pyramid nature, it is very effective to propagate the extracted feature from lower dilated convolutional layers (DCLs) to middle and higher DCLs, which result in better estimation accuracy. The FM is used to fuse the incoming features extracted by other modules. The proposed technique is tested through simulations on three well known datasets: Shanghaitech (Part-A), Shanghaitech (Part-B) and Venice. Results justify its relative effectiveness in terms of selected performance.

A battery-powered over-voltage triggering method for two-electrode spark gaps in Marx generator.

Triggered gas switches with trigger electrodes, such as three-electrode spark switches and trigatron type spark switches, have been widely used in repetitive operating Marx generators due to their large current flowing capability. However, the ablation of trigger electrodes during repetitive operation is inevitable, which reduces the working life of triggered gas switches. In this paper, we introduced an over-voltage triggering method for two-electrode spark switches by replacing one of the charging inductors of the Marx generator with a pulse generator. The experimental results showed that a high-voltage trigger pulse with an amplitude of 60 kV and a rising speed of 7.65 kV/100 ns was obtained from a single stackable module with a 12 V lithium battery pack as the energy source. Due to the absence of trigger electrodes, the triggering method is beneficial to extend the lifetime of switches and realize a maintenance-free repetitive operating Marx generator.

Two-Stage Optimization Method for Sizing Stack and Battery Modules of a Fuel Cell Vehicle Based on a Power Split Control

Currently, hybrid and battery electric vehicles are the best-selling green cars commercially available. However, there is a growing interest in fuel cell electric vehicles (FCVs). Nevertheless, due to the unidirectional nature of energy transformation in an FCV, an auxiliary energy storage system (ESS) is required to cope with peak power demand and recover braking energy. In this paper, we propose a joint algorithm for sizing both the fuel cell (FC) stack and an auxiliary storage system, taking into account the power split strategy between the two energy sources. Moreover, a novel power split method is introduced based on the lumped resistance of both the FC stack and lithium-ion battery modules. Several simulation results implementing different driving cycles prove that the proposed sizing procedure is able to reduce the fuel consumption of the FCV and increase the expected lifetime of both the FC stack and lithium-ion battery modules according to a given power split strategy.

Finite Element Simulation of Potential-Induced Degradation Kinetics in p-Type Silicon Solar Modules

We present a physics-based model to describe the kinetics of potential-induced degradation (PID) in p-Si photovoltaic (PV) modules, parametrized by the diffusivities of Na in the module stack, electric field in the SiN <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> , level of Na contamination, and segregation kinetics of Na in SiN <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> . Based on a sensitivity analysis on the expected electric field and levels of Na contamination at the surface of SiN <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> , we identify a relationship between the diffusivity of Na in the stacking faults present in the emitter and the kinetics of shunt resistance, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$R_{\mathrm{sh}}$</tex-math></inline-formula> . These findings indicate a faster diffusion mechanism through the stacking faults than that which would be expected for bulk Si, for PID-prone p-Si modules. Our simulations imply that a decrease in the SiN <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> resistivity alone cannot explain robustness to PID-s, suggesting that a diffusivity dependence on the nitride chemistry may be responsible in part for PID-robust devices. We show that additional interface engineering could potentially reduce the ingress of Na and hence PID by allowing Na to segregate on interface layers.

R²IPoints: Pursuing Rotation-Insensitive Point Representation for Aerial Object Detection

Anchor-free aerial object detection methods have recently attracted much attention due to their simplicity and efficiency. However, the performance is still unsatisfactory due to the following two main limitations. On the one hand, the anchor-free detector employs ordinary convolution layers with axis-aligned receptive fields to extract object features, resulting in lacking internal mechanisms to handle the rotation variance. On the other hand, the detector sacrifices much semantic information to achieve faster detection, leading to the inability to deal with objects’ high inter-class similarity and intra-class diversity. To address these issues, in this paper, we present a unique anchor-free detector, termed Rotation-Insensitive Point Representation (R2IPoints), of which a set of category-aware points are employed to encode the spatial and semantic information of the arbitrary-oriented objects. Specifically, we first devise a Stacked Rotation convolution Module (SRM) to encourage the learning of rotation-insensitive point representation by adaptively modelling orientation-agnostic interdependencies over stochastically rotated features. Meanwhile, we further introduce a Class-specific Semantic enhancement Module (CSM). It performs category-aware semantic activation to recalibrate features, thus enabling the point representation to be aware of object categories. Through jointly optimizing the two proposed modules in an end-to-end manner, R2IPoints could simultaneously generate rotation-insensitive and category-aware point representation. Extensive experiments on the challenging DIOR and DOTA datasets demonstrate the superiority of the proposed method. We achieve 72.7% mAP on DIOR and 74.34% mAP on DOTA, surpassing the baseline method of +2.4% mAP and +2.49% mAP, respectively. The code is available at https://github.com/shnew/R2IPoints.

Dual-Path Modeling With Memory Embedding Model for Continuous Speech Separation

Continuous speech separation (CSS) aims at separating overlap-free targets from a long, partially-overlapped recording. Though it has shown promising results, the origin CSS framework does not consider cross-window information and long-span dependency. To alleviate these limitations, this work introduces two novel methods to implicitly and explicitly capture the long-span knowledge, respectively. We firstly apply the dual-path (DP) modeling architecture for the CSS framework, where the within and across window information are jointly modeled by alternating stacked local-global processing modules. Secondly, to further capture the long-span dependency, we introduce a memory-based model for CSS. An additional memory pool is designed to extract embedding from each small window, and the inter-window commutation is established above the memory embedding pool through an attention mechanism. This memory-based model can precisely control what information needs to be transferred across the windows, thus leading to both improved modeling capacity and interpretability. The experimental results on the LibriCSS dataset show that both strategies can well capture the long-span information of the continuous speech and significantly improve system performance. Moreover, further improvements are observed with the integration of these two methods.

SRCANet: Stacked Residual Coordinate Attention Network for Infrared Ship Detection

The inability of conventional algorithms to detect infrared (IR) ship targets in complex scenes led to the development of detection methods based on convolutional neural networks (CNNs). In this study, we propose a CNN-based stacked residual coordinate attention network (SRCANet) for detecting IR ship targets. Three-directional stacked interaction modules and a full-scale skip connection feature fusion scheme are introduced. The proposed network maintains and integrates sufficient contextual information of IR ship targets and obtains clear target boundary information. A cascaded residual coordinate attention module (CRCAM) is designed as the basic node in the SRCANet. Additionally, a residual coordinate attention module (RCAM) is introduced, which combines a two-dimensional convolution layer with batch normalisation and rectified linear unit (CBR), a coordination attention module, and a residual connection. The RCAM enhances the input feature map and improves the representability of objects of interest. The CRCAM comprises several cascading RCAMs that deepen the feature extraction layers. Furthermore, because there is no publicly available IR ship target dataset for segmentation, pixel-level annotations are performed on a set of IR ship target images and released as a single-frame IR ship detection (SISD) dataset. Extensive experiments were conducted on the SISD dataset and the widely used single-frame IR small target dataset to demonstrate the superiority of the proposed method. The results indicate that the SRCANet outperforms the state-of-the-art models, and it is more robust when target texture information is lacking. The SISD dataset is available at https://github.com/echo-sky/SISD.

Performance evaluation of a 4-stack solid oxide module in electrolysis mode

To support the current trend of testing bigger reversible Solid Oxide Cell (rSOC) modules, CEA has built the 120 kWDC Multistack platform. It was used to test SOLIDpower recently developed-Large Stack Module (LSM) in electrolysis mode.Results show high thermal performance of the LSM, with homogeneous temperature distribution and losses in the kilowatt range above 700 °C. A performance map was recorded between 712 and 744 °C over 22.4-to-29.6 kg h−1 steam flowrates using a fast control strategy to avoid endothermic operation. A peak power of 74 kWDC was converted into more than 50 kg day−1 of H2 (35.5 kWhDC kgH2−1). In addition, fuel utilization of more than 90% and steam conversion above 80% were demonstrated at the module level. In the end, the modular design of the LSM seems well suited for system scale up, paving the way for mutualization of auxiliaries and CAPEX reduction.

Design, manufacturing, and operation of movable 2 × 10 kW size rSOC system

AbstractReversible solid oxide cell (rSOC) technology enables both electricity generation for local demands and electricity conversion into hydrogen with high power‐to‐gas (AC to H2) efficiency. This work describes modeling and implementation of movable “10‐feet container” size rSOC system in a pilot demonstration scale (10 kW SOEC/2 kW SOFC). The selected two‐stack two‐module system layout is the simplest option to investigate multi‐module rSOC systems in various operation modes and conditions. Special attention is also paid to heat integration: heat losses are minimized with optimized BoP component design, placement, and insulation. Reversibility, dynamic operation, and methods for efficient transitions between SOFC and SOEC modes are investigated at a system level. The developed system is highly instrumented enabling detailed system analysis, for example, the calculation of enthalpy flows and efficiencies of all BoP components. Analyses of key parameters on the performance and efficiency are presented. To explore upscaling of rSOC systems, the effects of size and structure of stack modules on the reliability and maintenance of the entire system are investigated, and as a conclusion, the construction of multi‐MW scale rSOC systems are recommended to be implemented with approximately 100 kW size stack modules.

Design and Implementation of Environmental Monitoring System Using Photoelectric Wireless Sensor Network

Photoelectrochemical (PEC) sensing is a light-induced electron transfer sensing method based on photoelectric signal conversion to represent the electrode/solution interface. The analysis method is easily disturbed by environmental factors such as light intensity, which reduces the detection accuracy. PEC biosensor with sunlight as the light source is designed. CC2530 RF hardware module and Zstack software protocol stack produced by TI company are adopted to establish PEC wireless sensor network (WSN) for farmland environment detection. 5G-DTU module is selected to realize the remote data transmission between the PEC WSN and remote server. The data exchange is carried out through a 5G mobile communication network. The farmland environment monitoring adopts B/S architecture. The types of all PEC sensors need to be selected and the hardware circuit module is designed. The corresponding interface adopts a unified standard 485 bus communication interface to facilitate the data reading of the sensor. In the experiment, the designed PEC sensor can distinguish between carbon nitride nanosheets (CNNS) and nitrogen fossil graphene loaded silver/TiO2 (Ag/TiO2/D3NGH) by changing the bias potential, and the sensor can work in unstable sunlight. The photocurrent of anode and cathode of the PEC sensor increases with the increase of sunlight intensity, and the photocurrent ratio remains constant with the further increase of the concentration of double electrodes (CNNS, Ag/TiO2/D3NGH). In the field mold test, the designed PEC sensor shows good selectivity for aflatoxin B1 (AFB1), and the AFB1 is recovered from bean products in the field, with a recovery rate of more than 95%, which proves that the photoelectric monitoring system can accurately measure and analyze the field mold.

Multi-level refinement enriched feature pyramid network for object detection

Class Imbalance and scales imbalance are common in object detection. A class imbalance occurs due to insufficient inequality between the number of instances with respect to different classes, while an imbalance in scale occurs when object have different scales and a different number of examples of different scales. In order to solve the problem of scale variance (scale imbalance) and class imbalance together, we propose a simple and effective feature enhancement scheme that explicitly uses all information of a multi-level structure to generate a multilevel contextual features pyramid with multiple scales. We also introduce a cascaded refinement scheme that incorporates multi-scale contextual features into the Single Shot Detector (SSD) predictive layers to improve their distinctiveness for multi-scale detection. A stack of multi-scale contextual feature modules is used in a feature enhancement scheme to merge the multi-level and multi-scale features. Then we collect the equivalent scale features over the Multi-layer Feature Fusion (MLFF) unit to construct a feature pyramid in which each feature map is made up of layers from multiple levels. More robustness and contextual information are integrated into the pyramid through chain parallel pooling operation. To improve classification and regression, a cascaded refinement scheme is proposed that effectively captures a large amount of contextual information and refines the anchors to solve the class imbalance problem. The experiments are carried out on two benchmarks datasets: MS COCO and PASCAL VOC 07/12. Our proposed approach achieves state-of-the-art accuracy with an AP of 40.6 in the case of multi-scale inference on MS COCO Test-dev (input size 320 × 320). For 512 × 512 input on the MS COCO Test-dev, our approach leads in an absolute gain in precision of 1.8% compared to the best reported results of single-stage detector (AP: 45.7).

Effect of scaling-up on the performance and degradation of long-term operated electrolyte supported solid oxide cell, stack and module in electrolysis mode

The scale-up from single cell, to stack and integrated stack module induces an increased degradation that needs to be assessed for improving the performance and the lifetime of the high-temperature electrolyzer system. Within this work, the degradation rate of a 30-cell stack and a 90-cell module (based on similar 3YSZ electrolyte supported solid oxide cell) is evaluated with operation time of 4,224 h (−0.5 Acm−2) and 2,200 h (from −0.5 to – 0.3 Acm−2), respectively.The voltage degradation rate of the 30-cell stack is 10.5 mV/kh (0.8%/kh referring to the thermoneutral voltage of 1.3 V) including the temperature correction factor for an increase of 2 °C/kh. It is at present/currently the lowest degradation value reported for a stack of 30 electrolyte supported cells considering the thermal factor correction. This implies an increase of 5.5 mV/kh for the stack compared to the single cell. Along the steady-state phase two dynamic phases of 8,500 and 7,500 ON/OFF cycles were performed every 2 min. It has been shown that these significant load cycles do not induce additional degradation.Moving to the 90-cell module, irreversible damage occurred due to incidences related to the balance of plant; based on their analysis, propositions are made to counteract them.

Highly Efficient SOFC System with Mk35x-Stack Module Operated with Gasified Biomass - HieffBioPower -

A stationary SOFC system utilizing gas from a biomass updraft gasifier is under development within the project “HiEffBioPower” by MAWERA, BIOS, Bosal, AVL, Calida Cleantech, Fraunhofer IKTS, Wuppertal Institute and University Utrecht (European Union Horizon2020 No. 727330). The system is based on Mk352-CFY-stack with Chromium based (CFY) interconnects and electrolyte supported cells. These stacks are robust, redox stable and demonstrated low degradation rates. The stacks are arranged to modules of eight 30-cell stacks and are electrically connected in series. The stack module was pre-tested at the test rig at IKTS and showed performance in accordance with system requirements. Stationary power points were measured to compare the behaviour of the stack module with the system tests at MAWERA. AVL designed and installed the overall SOFC system (heat exchangers, stack, catalytic afterburner, process control and visualisation) with a nominal capacity of 6 kWel. After shipping all parts of the SOFC system to MAWERA in Hard (AT), the coupling of the biomass gasifier, the gas cleaning unit and the SOFC system was conducted. The system is operated by an automated process control. The highest efforts needed the novel gas cleaning unit, consisting of a high temperature filter, a desulphurisation unit as well as a novel catalytic gas reforming stage developed by BIOS.The biomass gasifier has been operated with wood chips of different quality. The high tar contents of the product gas make a tar cracking essential to reach a high calorific value of the gas for the SOFC system. The novel gas cleaning system shows good performance and reaches the defined SOFC requirements. The SOFC system developed and operated by AVL reached stable conditions for several days. The operating results of the stack module at lab conditions and at the real system are in a good agreement. The SOFC stack module performance in terms of cell voltages, stack temperature distribution and fuel utilization under biomass reformate fuel will be shown and supported by several simulation to get an overall energy balance. Examples of optimization for several components by CFD simulation and component tests will be given.

Progress in SOC Development at Fraunhofer IKTS

Despite long and successful development history of Solid Oxide Cells (SOC) continuous improvement in performance, longevity, manufacturing and system integration is necessary to bring this highly efficient technology to the market. The activities on material development and optimization at IKTS are focused mainly on enhancement of durability in SOFC, SOEC and rSOC operation and on boosting the power density for electrolysis operation. The recent results show considerable enhancement in cell longevity and power density.The SOC priorities in applied research are moving from materials to industrialization and Fraunhofer as applied R&D service provider is following this trend. During recent years considerable efforts on simplification and automation of cell and stack manufacturing processes has been addressed at IKTS. The manufacturing processes for electrode manufacturing have been adjusted for high yield automated printing on thin electrolytes with integrated quality control measures. The efficient ways for reduction of time and energy consumption for sealing process of SOC stacks have been found and shown in pilot production as well as automated assembling of components to stacks demonstrated. Furthermore the stack integration into modules utilizing stacks of different providers has been addressed during last years and reliable solutions for building of compact stack modules of higher power (2 to 10 kW) has been developed and tested with industrial partners.The increasing interest in “green hydrogen” generated multiple opportunities for SOEC technology to be considered as inherent part of industrial and chemical processes. IKTS pioneering work on coupled operation of SOEC module with Fischer-Tropsch reactor provided first demonstration of feasibility of this approach for waxes production. The process design and analysis of steel, using SOEC technology, has been performed and resulted in multiple demonstration activities in last four years. Recently novel approaches for ammonia production are addressed at IKTS showing opportunity for transportable chemicals/fuel production without need of CO2 source.High power electrolysis applications (>10 MW) will need new approaches for stack design and put higher requirements on durability. High power electrolysis applications will also open a new window of opportunities for industrial SOFC power plants. This vision is considered as outline for our future research.