Adaptive backstepping control for ship nonlinear active fin system based on disturbance observer and neural network

Adaptive backstepping control based on disturbance observer and neural network for ship nonlinear active fin system is proposed. One disturbance observer is given to observe the disturbances of the system, by this way, the response time is shorten and the negative impact of disturbance and uncertain elements of the system is reduced. In addition, radial basic function neural network (RBFNN) is proposed to approach the unknown elements in the ship nonlinear active fin system, therefor the system can obtain good roll reduction effectiveness and overcome the uncertainties of the model, the designed controller can maintain the ship roll angle at desired value. Finally, the simulation results are given for a supply vessel to verify the successfulness of the proposed controller

Carbon assessment for cocoa cropping systems in Lampung, Indonesia

Cocoa (Theobroma cacao L.) production plays a key role in the economics of Indonesia, the world’s fourth largest cocoa bean producing country. With more than 1.6 million hectares of land planted with cocoa, small improvements in emissions efficiencies or carbon sequestration opportunities can have a relatively large mitigating effect on emissions from agroforestry and land use. The carbon assessment in Lampung, Sumatra was done to evaluate environmental impacts of cocoa as a commodity through estimation of carbon stock and carbon footprint, GHG emissions during the cultivation of cocoa in different cropping systems. Segmentation of cropping systems along density of intercropping, inputs use intensity and residue management practices identify opportunities for climate smart practices tailored to each segment

Effects Of Filler Loading And Different Preparation Methods On The Properties Of Cassava Starch Filled Natural Rubber Composites

This project is concerned with the preparation methods of cassava starch/natural rubber composite. First, the effects of different cassava starch (CS) loading on the properties of natural rubber (NR) composites were investigated. CS/NR composites at different CS loading (5-25 phr) were compounded using a tworoll mill under room temperature. Results indicated that the scorch time and cure of CS/NR composites slightly reduced with increasing CS loading. However, the addition of CS reduced the maximum torque, thermal stability and mechanical properties of CS/NR composites. The optimum tensile strength and tear strength were obtained at 10 phr of CS and decreased considerably with higher CS loadings. Second, the effects of CS loading and preparation methods on properties of CS/NR composites have been investigated. Three types of preparation methods were used in this project namely; direct blending, co-coagulation, and freeze-drying. The CS/NR composites prepared by co-coagulation and freeze-drying methods exhibit better mechanical properties and thermal stability than CS/NR composites prepared by direct blending method. In all methods studied, the hardness increased with increasing CS loading. Moreover, the morphological study confirmed that the freezedrying method improved the dispersion of CS in NR matrix than direct blending and co-coagulation methods

少量の石灰・セメントで処理した粘性土の挙動

博士(工学)佐賀大

Quality comparison of Y-shape joints by tube hydroforming with and without counterforce

The design capability, strength, and structural rigidity provided by tube hydroforming (THF) are successfully used in many applications to produce high-strength parts and assemblies with improved mechanical properties, optimized service life, and weight features. In tubular metal forming, output parameters such as branch height, distribution of tube wall material thickness, distribution of damage factor, metal flow, effective stress, and effective strain significantly affect the quality of the product after the forming process. Therefore, this paper aims to evaluate the manufacturing quality of Y-shape joints from AISI304 material steel tube through output parameters of THF process with and without counter punch force on numerical simulation base. The Finite Element Method (FEM) has become an established feature of metal forming technology. The objective of FEM is to replace costly and elaborate experimental testing with fast, low-cost computer simulation. The simulation study uses finite element method-based virtual prototyping techniques to characterize output parameters, gain insight into strain mechanics, and predict mechanical properties of shaped components. The research results are presented clearly and unambiguously through the evaluation of 7 criteria to compare the quality of the specimens hydroformed by two surveyed cases and optimize the crucial input process parameters. And these data can be applied in experiments, more efficient product and process design, calculation, and control of input parameters avoiding costly trial and error in industrial production. The findings can help technologists optimize process parameters in the hydroforming process of products with protrusion from a tubular blan

Multitasking Correlation Network for Depth Information Reconstruction

In this paper, we propose a novel multi-tasking network for stereo matching. The proposed network is trained to approximate similarity functions in statistics and linear algebra such as correlation coefficient, distance correlation and cosine similarity. By doing this, the proposed method decreases the amount of time needed to calculate the disparity map by using CNN's ability to calculate multiple pairs of image patches at the same time. We then compare the execution time and overall accuracy between the traditional method using functions and our method. The results show the model's ability to mimic the traditional method's performance while taking considerably less time to perform the task

Differentiable Bayesian Structure Learning with Acyclicity Assurance

Score-based approaches in the structure learning task are thriving because of their scalability. Continuous relaxation has been the key reason for this advancement. Despite achieving promising outcomes, most of these methods are still struggling to ensure that the graphs generated from the latent space are acyclic by minimizing a defined score. There has also been another trend of permutation-based approaches, which concern the search for the topological ordering of the variables in the directed acyclic graph in order to limit the search space of the graph. In this study, we propose an alternative approach for strictly constraining the acyclicty of the graphs with an integration of the knowledge from the topological orderings. Our approach can reduce inference complexity while ensuring the structures of the generated graphs to be acyclic. Our empirical experiments with simulated and real-world data show that our approach can outperform related Bayesian score-based approaches.Comment: Accepted as a regular paper (9.37%) at the 23rd IEEE International Conference on Data Mining (ICDM 2023

NEW METHODS AND NEW PARADIGM TO DESIGN SENSOR NETWORKS FOR PROCESS PLANTS

Due to economic reason, not every process variable can be measured by a sensor. The problem of optimum selection of sensor location is referred to as sensor network design problem (SNDP).Being a combinatorial optimization problem, the SNDP poses significant computational challenges for researchers, especially for large scale problems. The methods to solve the SNDP can be divided into two three classes: mathematical programming, graph-theoretic methods and stochastic methods (e.g. genetic algorithm)The SNDP problem itself can be divided into two big classes: designing sensor network intended for process monitoring purpose (to obtain accurate process data) and designing sensor network for process fault diagnosis and resolution. The former can be solved by many methods while the latter is usually solved by graph-theoretic methodsAlthough extensive researches have been done on this problem, efficient methods to design sensor networks for large scale nonlinear problems have not yet been found. Moreover, all the published models are developed from technical point of view, which requires knowledge / expertise of the users to use appropriate constraints / specifications in the model. A model that bases solely on an economic viewpoint has not yet been proposed.Addressing the mentioned drawbacks is the objective of this work. More specifically, in this work:i)Efficient computational methods to solve SNDP for large scale nonlinear problems are proposed.ii)A value-optimal SNDP is proposed and solved by using appropriate methods

PSO based Hybrid PID-FLC Sugeno Control for Excitation System of Large Synchronous Motor

This paper proposes a hybrid control system integrating a PID controller and a fuzzy logic controller, using the particle swarm optimization (PSO) algorithm to optimize control parameters. The control object is an excitation system for a large synchronous motor, which is widely used in large power transmission systems. In practice, the change in load and excitation source can affect the operating mode of the motor. Therefore, a hybrid controller is designed to stabilize the power factor, resulting in better working performance. In the control algorithm, a PID controller is initially designed using PSO to optimize the control coefficients. The FLC-Sugeno control is then integrated with the PID, in which PSO is utilized to optimize membership functions. Numerical simulation results demonstrate the advantages of the proposed approach. Doi: 10.28991/ESJ-2022-06-02-01 Full Text: PD

Isolation and determination of Vibrio spp. pathogen from Sciaenops ocellatus suffering from hemorrhagic disease under cage culture in Vietnam

This study was carried out to isolate and determine the Vibrio spp. from the Red drum fish (Sciaenops ocellatus) suffering from the hemorrhagic disease in Vietnam.  In this study, 18 strains of Vibrio bacteria were identified from 27 samples of Red drum fish. The isolated bacterial strains were identified with the 16S rRNA sequencing method and checked for morphological, physiological, and biochemical characteristics by using the API 20E KIT. Results of the study revealed the presence of twelve strains of V. alginolyticus, three strains of V. fluvialis, and three strains of V. orientalis. All Vibrio strains have gene similarities with those on the Genbank ranging from 98.05 to 100%. The biochemical characteristics of these 18 isolates were similar and these are susceptible to tetracycline and doxycycline and entirely resistant to ampicillin, amoxicillin, and erythromycin