KTU Open Journal Systems (Kaunas University of technology)
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Enabling Change: Analysis of European Union Civilian Missions through the Lens of Theory of Change
No full textThis paper examines the implementation process of European Union (EU) civilian Common Security and Defence Policy (CSDP) Missions using the Theory of Change framework, focusing on resources, outputs, and impact. The research employs a qualitative methodology, drawing on empirical data from in-depth interviews, conducted in December 2024, with purposively selected European External Action Service (EEAS), Member state representatives, CSDP Missions management representatives, directly involved in the planning and execution of CSDP missions. Despite the limitations of this study, such as only partial coverage of EU missions due to the sensitivity of the topic and limited access to information, it provides valuable insights into the process of mission implementation and the challenges that influence their outputs and impact. Based on interviews with selected experts, the study identifies strategic incoherence, limited expertise and fragmented coordination efforts as key obstacles, especially in advancing digital transformation. Although short-term outputs are visible, long-term technological progress is hampered by systemic inertia and institutional limitations.
An Analytic Study on the Enhancement of Heat Transfer with Nanofluid
No full textThe article studies the problem of boundary layer fluid flow for two dimensional steady and incompressible hybrid nanofluids over the nonlinear stretching surface. The problem is analysed for the distribution of velocity, temperature, and concentration profile influenced by the parameters taken for study. The pertinent parameters analysed in the boundary value problem are magnetic, non-linear stretching parameter, Prandtl number, radiation, Brownian, thermophoresis. The governing sets of PDE’s are converted to a set of ODES using the similarity transformations. The numerical results are obtained by RK algorithm techniques. The study portrays the results for skin friction and Nusselt number for varying the parametric values. The interpretation of results is done graphically and in tabular format. The detailed parametric study is explored for the enhancement of heat transfer, the influence of the parameters on velocity, temperature, and volume fraction of the nanofluid, skin friction coefficient, and Nusselt number. The wide applications of the problem taken for the study where the control of complex parametric influence in heat transfer is applied in the field of energy recovery, biomedical, and industrial systems in the present global scenario
Comparative Study for Corrosion Resistance of Ferrous and Non-ferrous Pipes Immersed in Magnetized Water
No full textThis study aims to investigate the corrosion resistance of galvanized steel (GS) and AZ31 pipe materials immersed in a magnetized water treatment (MWT) applied river water source. The potentiodynamic corrosion test results showed that a better corrosion rate is obtained by the AZ31(corr.rate: 0.0442 mm/y) specimen than GS (corr.rate: 0.0764 mm/y) due to the passivation efficiency resulting from the calcite and MgO particles on the surface
Research on Efficient Dynamics Simulation Technology for Artillery Equipment
No full textIn response to the efficiency bottleneck in the overall design phase of artillery, this paper proposes an agile design tool for artillery based on the RecurDyn solver, IAOD. This tool achieves rapid evaluation and optimization design of artillery dynamics simulation models through parametric modeling and integration of shooting test data. The IAOD system adopts a fully parameterized driven template model, combined with machine learning, parameter identification, and multi-level optimization algorithms, significantly improving the design efficiency of large caliber self-propelled artillery. The system innovatively applied a sparse single hidden layer neural network proxy model and a simulation parameter identification method based on test data, achieving multi-objective collaborative optimization. The effectiveness and practicality of the IAOD system have been verified through the practical application of a vehicle mounted artillery design case, demonstrating its potential for application in the field of artillery design. This study has significant military and defense value in improving the efficiency and quality of artillery equipment development
Asymmetric Helical Gears Bending Stress Calculation Formula
No full textAiming at the limitation that the traditional symmetrical gear stress calculation formula cannot be applied to the new asymmetric helical gear, a calculation method of tooth root bending stress of asymmetric involute helical gear are proposed in this paper. According to the meshing principle, the digital tooth surface model of asymmetric involute helical gear is established. The distribution characteristics of tooth root bending stress are analyzed by finite element method, and the relationship between tooth root bending stress and pressure angle is studied. On this basis, the influence coefficient of pressure angle is proposed. Combined with multiple regression analysis, an analytical formula for calculating the bending stress of tooth root without finite element method is proposed. By comparing with the calculated values of the finite element method, the error rate of the theoretical formula is 6.52%, which verifies its accuracy. The research results show that the asymmetric helical gear exhibits excellent tooth root bending bearing capacity under high pressure angle conditions, which provides key theoretical support and calculation tools for the design of high-performance asymmetric gears
Analysis of Material Optimization and Performance Parameter Difference of Expandable Sand Screen
No full textSand control technology of expansion sand screen is one of the effective methods to solve the sand problem in oil wells, due to its unique working mode, which has significant benefits in improving production and reducing costs, and has been widely used in the oilfield field. However, the operational conditions that expansion sand screens are subjected to, such as high temperatures, high pressure and high corrosion, exert greater demands on the material performance of the expansion sand screens. The advancement of this technology is predominantly constrained by the intrinsic characteristics of the material in question. In this study, five materials were selected as the base tube materials of the expansion sand screen, namely stainless steel 654SMo, stainless steel Incoloy 27-7Mo, stainless steel 2507, stainless steel Incoloy 625 and stainless steel 316L. These materials were chosen based on their performance requirements in relation to the expansion sand screen. The impact of diverse materials on the expansion performance of base tubes was investigated through a multifaceted approach, integrating tensile experiments, prototype expansion experiments, and finite element analysis. The experimental results demonstrate that stainless steel 316L is a more suitable base tube material under the condition of relatively low requirements for temperature and corrosive environment. Conversely, stainless steel Incoloy 27-7Mo is a more suitable base tube material in more demanding deep well environments. Finally, stainless steel Incoloy 625 is a more suitable base tube material for working conditions with higher requirements for corrosion resistance and mechanical properties. A comparison of the results of the finite element analysis with those of the prototype expansion experiment indicates that the volume percentage of the high stress region is a useful indicator of the expansibility of the base tube to a certain extent. The findings of this study provide a novel evaluation metric for the optimisation of parameters and the selection of materials for expandable sand screen. This has the potential to reduce costs and time during the research process
Detection and Classification of Blood Cells Using Different Deep Learning Approaches
No full textBlood cells have an important place in the human immune system. The amount of blood cells in the blood is used to determine whether human health is normal or unusual. For this reason, detecting and determining the amount of RBC, WBC and Platelets elements in the blood is very important for human health. In the management of all these processes, basic factors such as the complexity of cell structures, loss of time, and the necessity of expert opinion make the realization of these processes very complicated. In this study, cell detection was carried out using models of Detectron2 and Yolo algorithms to automatically detect and quantify blood cells quickly. The BCCD dataset was used to run the models. In the study, the performance results of the models of 21 different most recent artificial intelligence algorithms in detecting blood cells were analyzed. In this comprehensive research, the accuracy values of the train and test process of the models were examined comparatively, and the most suitable model was determined, which aims to provide a high success rate for small models. As a result of the study, the highest AP value with 93.7% in the train result among 5 models belonging to Detectron2 from 21 models belongs to the Faster R-CNN X_101_32x8d_FPN_3x and Faster R-CNN R_101_C4_3x models; Among the 16 models belonging to Yolo, the highest AP value as a result of the train belongs to the Yolov7-w6 model with 95.8%. When the test results are examined, among the Detectron2 models, the Faster R-CNN R_101_FPN_3x model achieved 90.3% AP value. In addition, among the Yolo models, the Yolov5-s model was the most successful algorithm with an AP value of 94.5%. When the train and test results of the models, training time, weight size values were examined, it was determined that the Yolov5-s model was the most successful model in classifying and detecting blood cells in the BCCD dataset
Single-Pulse Detection Method of Radar Weak Target Based on a Two-Stage Deep Neural Network
No full textWith the increasing prevalence of drones in low-altitude airspace, the radar detection of weak targets with a low signal-to-noise ratio (SNR) still poses a crucial challenge. Traditional constant false alarm rate (CFAR) methods encounter issues of high false alarms and low accuracy when the SNR is below-15dB. This paper puts forward a two-stage deep neural network to improve weak target detection by emulating human visual perception. In the first stage (coarse detection), potential targets are rapidly localized through grid-based regression. In the second stage (fine detection), depth-wise separable convolution (DSC) and residual connections are utilized for accurate classification. Experimental results show that, at an SNR of -20dB, the detection rate of the proposed method is 20% higher than that of CFAR methods, and the inference speed is 3.66 times faster than that of single-stage networks. Ablation studies confirm the efficiency improvements brought by the coarse detection network. This approach offers a robust solution for real-time drone surveillance in complex and cluttered environments.
A TCGAN-Based Real-Time Personalized Motion Guidance System to Reduce Compensatory Movements in Post-Stroke Rehabilitation
No full textStroke rehabilitation is essential for motor function recovery, yet traditional methods require therapist supervision, which can be costly and inaccessible. Home-based rehabilitation offers an alternative, but without real-time guidance, patients may develop compensatory movements, hindering progress. Existing approaches provide feedback only after exercises are completed, limiting their effectiveness. To address this, we propose a Temporal Conditional Generative Adversarial Network (TCGAN)-based motion generation system that provides real-time skeletal guidance tailored to each patient’s body structure and positioning. By detecting key anatomical landmarks and generating adaptive motion sequences, the system ensures precise movement execution, reducing errors and improving rehabilitation outcomes. Both qualitative and quantitative evaluations confirm the effectiveness of the generated exercises, benefiting from the proposed architecture, improved loss function, optimized training process, and TCGAN hyperparameter tuning. Experimental results show a high degree of similarity between generated and real movements, with a Fréchet Inception Distance (FID) score of 0.87, demonstrating the system’s realism and reliability. This approach enhances patient autonomy and recovery efficiency, offering a more interactive and adaptive rehabilitation experience.  
A Novel Employee Re-identification Method Based on Attention-free Capsule Network for Factory Surveillance Images
No full textTraditional methods have low recognition rate and poor robustness when dealing with complex factory employee monitoring images, so a new employee re recognition method based on Attention-free Capsule Network for factory monitoring images is proposed. Least Squares Generative Adversarial Network (LSGAN) is used to restore the factory monitoring image to repair the missing or damaged image caused by lighting, occlusion, noise, etc. Wavelet Contourlet transform is used to improve the details and clarity of images and the accuracy of subsequent staff re recognition. The mixed Gaussian model (GMM) is used to accurately segment the employee foreground in the image, and the segmented employee foreground image is input into the Attention-free Capsule Network. The feature is extracted through multi-layer convolution and pooling operations, and the dynamic routing mechanism is used to extract and aggregate employee identity features. After training, the employee identity tags are output to achieve efficient employee re recognition of factory monitoring images. The experimental results show that compared with visual attention, KISS+, and center and scale prediction methods, the proposed method introduces a novel approach for employee re identification in factory monitoring images. The proposed method demonstrates strong anti-interference and adaptability. In the comparison of key indicators, the proposed method achieved a recognition accuracy of 95.8 on Rank-1, 98.4 on Rank-5, and 99.5 on Rank-10. This series of numerical comparison results fully demonstrates that the proposed method has strong image processing capabilities, as well as high recognition accuracy and robustness.