In this paper, we consider the dynamic output feedback control within the LMI framework for robust stabilization and L2 gain analysis of a class of interconnected systems composed of linear subsystems and nonlinear timevarying interconnections satisfying quadratic constraints. By a novel decentralized structure of dynamic output feedback, we propose a sufficient LMI condition. And a design method is applied for solving BMI problem to obtain the feedback gain matrix. An example of inverted pendulums is used to illustrate the applicability of design method.

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In this paper, we consider to control a constrained nonlinear system by using the nonlinear model predictive control (NMPC) framework. The control problem is reduced to solving nonlinear algebraic equations at each sampling period. An algorithm used to solve the equations is called the Continuation/Generalized Minimum Residual(C/GMRES) method, which efficiently utilizes characteristic of the NMPC framework that optimization calculations are performed at each sampling period. Simulation results show the applicability of NMPC for mass spring damper system with nonlinearity as a specific example of constrained nonlinear systems.

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A disturbance observer is considered to estimate disturbances that interfere with the motion of a controlled system together with the state. In this paper, we design a disturbance observer for higher-order disturbances by applying the same-dimensional observer concept. then, we extend the discussion to a servo system by using a high-order disturbance observer, and apply to tracking control of fourwheeled vehicles. furthermore, the forces consider that four-wheeled vehicle is generated to while driving, such as wind and viscous friction with the road, so as to achieve both disturbance estimation and tracking objective.

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Electroluminescence (EL) imaging is a reliable technique for inspecting photovoltaic (PV) modules, as its high spatial resolution enables the detection of even minute flaws on the module surfaces. However, conventional EL image analysis, which is typically performed manually, is expensive, time-consuming, and requires expert knowledge of a wide variety of defects. In this study, we propose a method for the automatic detection of solar defects within a single EL image of a solar PV cell. Our approach utilizes a lightweight convolutional neural network (L-CNN) developed from scratch for feature extraction and a fully connected artificial neural network (ANN) for classifying functional and defective solar cells. The model is trained on 400 solar cell images extracted from high-resolution EL images of monocrystalline PV modules. The CNN model achieved an accuracy of 93.0% in detecting defective solar cells from the test dataset. Our model is light, making it easy to run on arbitrary hardware, and doesn’t require plenty of EL images for training. Our proposed method will facilitate continuous, fast, and accurate solar PV plant quality inspection. Proper maintenance of solar PV panels improves their efficiency and power output.

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Microgrids (MGs) with a high penetration of renewable energy are becoming increasingly popular, mainly due to the need for a sustainable and environmentally friendly power system. However, the stochastic characteristic of renewable energy sources makes operating a microgrid challenging. Therefore, having an effective energy management strategy (EMS) for a microgrid is essential. This paper proposes an optimized fuzzy logic control-based EMS for a microgrid with a fuel cell system (FC), photovoltaic array (PV), electrolyser, and battery bank. The main objective of the designed EMS is to ensure the microgrid's reliability while saving as much hydrogen as possible. Utilizing the proposed hybrid optimization algorithm, the optimizer changes the fuzzy memberships and fuzzy rules according to the differential power between the PV system and the loads, the battery state of charge (SoC), and the level of hydrogen stored in the hydrogen tank (LoH). To check the performance of the proposed optimizer, a comparative simulation is carried out in MATLAB/SIMULINK. The results have proved the proposed algorithm’s superiority over other optimizers.

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Intent detection and slot filling are essential tasks in natural language understanding (NLU) and natural language processing (NLP). They are commonly used in dialogue systems, chatbots, virtual assistants, and various applications involving human-computer interaction. Previously, traditional models could only perform one of these tasks at a time. However, recent advancements in natural language processing have allowed the combination of both tasks in a single model, resulting in significantly improved results compared to previous models. The development of the Bidirectional Encoder Representation from Transformer (BERT) model has played a significant role in enhancing the performance of these tasks. This is due to the Attention mechanism utilized by the BERT model. In JoinIDSF architecture, the authors used Attention mechanism additionally to explicitly incorporate intent context information into slot filling named the “intent-slot attention” layer. Based on that, to enhance efficiency on the slot filling task, we suggest a new architecture using a combination of BERT,CNN (Convolutional Neural Network), and “intent-slot attention” layer. The outcomes of our proposed architecture have demonstrated that it achieves state-ofthe-art results on the slot filling task, with a high F1 score. Furthermore, our approach has significantly improved the accuracy of the sentence-level semantic frame when tested on publicly available benchmark datasets such as phoATIS. Our model increased (0.02–0.2%) and (0.02–0.37%) for slot filling F1 score and semantic accuracy respectively. Additionally, our proposed architecture also showed a slight improvement in the intent detection task.

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In the world of mobile applications, protecting the user’s privacy is paramount. With the rise of mobility applications that make use of location-based services, data protection issues have become even more pressing. In this paper, we explore how to ensure compliance with data protection laws, specifically the General Data Protection Regulation (GDPR), when developing a new mobility application. Our focus is on the Geographical Location Oriented Network (GLocON), a cutting-edge framework for geo-locationoriented device-to-device communications. Through a security hackathon and a Data Protection Impact Assessment, we conducted an anonymization analysis of G-LocON and proposed measures to reduce the processing of personal data. The paper concludes that personal data can be anonymized by randomizing the unique identifier of peers and hiding location information by default for fair and transparent processing. Adherence to the GDPR is further ensured through the implementation of measures such as permanent storage for lawfulness. This work represents a significant step towards building more privacyfocused mobility applications.

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Waste collection is an important activity in every city, and this activity might have expensive costs to be accomplished. In this study, a route optimization for waste collection routes was performed with the use of the tool Vehicle Routing Problem in the software ArcGIS Pro. The data provided for the evaluation of the route optimization is from the waste collection company in charge of the areas of Tokiwa and Okubo neighborhoods in the city of Urawa, Saitama, Japan. The optimization results indicate a reduction in mileage between 7% and 13% and a reduction in time of operation in all the evaluated areas.

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Electric wheelchairs can move like pedestrians on sidewalks owing to their limited speed. These wheelchairs are attracting increasing interest as mobility aids for short commutes of approximately one mile from residential locations. When installing a safe and secure automated driving system in an electric wheelchair, the control of the electric wheelchair via the automated driving system must ensure that the set route can be followed with a high degree of accuracy. This study evaluated the maximum distance that the vehicle deviates from the set travel path and the accuracy of the stop position of an automated braking system for a set driving route using an electric wheelchair. The wheelchair comprises an additional automated driving function. As a preliminary experiment, a driving test was conducted on a driving route, including obstacle avoidance and right and left turns, to evaluate the control accuracy of the automated driving system guiding the electric wheelchair. As an experiment for control accuracy, we classified the course into curved roads, obstacle avoidance, and straight roads. Moreover, we investigated the effects of each route on the control accuracy and automated braking system. The results showed that the lateral error during driving tended to increase or decrease for the straight path. In addition, the error for the avoidance route was smaller than that for the curves. The results suggest that the evaluated Autoware control algorithm affects straight paths, while the time of steering and the magnitude of steering affect paths that include right and left turns.

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In Nepal, Division Road Offices (DROs) implement most bridge construction contracts in the Design-Bid-Build (DBB) approach. On the other hand, a new institutional setup called Project Offices (POs) implements a new contracting approach Design–Build (DB). It was expected that using a new approach and institution to execute the bridge projects would lead to a reduction in time and cost overrun, but the actual effect is still unclear. Therefore, the main objective of this research is to evaluate the effectiveness of the DB approach in POs and the DBB approach in DROs management for time and cost overrun risk mitigation in bridge construction works. This research is performed using the Mann-Whitney test for non-parametric samples, cumulative probability density, histogram analysis, and Pearson's correlation analysis. In the Mann-Whitney test, a hypothesis was set for finding the effect of institutional setups and contracting approaches on time and cost overrun. Furthermore, Pearson’s correlation analysis between time and cost overrun in respect of contracting approach and institutional setup was performed. It was found from the Mann-Whitney test that there is a significant difference between the DB contracting approach with POs and DBB contracting approach with DROs management towards time overrun risk minimization. However, there was no significant difference between those two arrangements for cost overrun minimization. The result of the correlation analysis showed a significant relationship between time and cost overrun. Finally, it was concluded that adopting new risk-sharing mechanisms and establishing dedicated institutions have fundamental achievements for improving the implementation performance of infrastructure construction.

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The importance of the cultural landscapes that compose different parts of our planet can be justified by the uniqueness and specific cultural and biological richness presented by the interaction between the human being and the surrounding natural environment. In order to preserve its valuable structure, studies about its resilience are extremely necessary in the current circumstances of the society since many changes are happening quickly and unexpectedly. Considering this context, the present study aimed to identify the beneficial action of sustainable tourism, an important tool for sustainable development, in contributing to a more resilient cultural landscape in Taketomi Island, Okinawa Prefecture, Japan. Through the use of cultural landscape resilience indicators applied to the local people, opinion questionnaires applied to the touristic community and an analytical landscape mapping, it was possible to understand the latent power of sustainable tourism in the region to exploit the economic potential of the island while contributing positively to its landscape resilience.

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Flexible electrodes made of cotton fabric with silver nanoparticles (AgNP) and polyaniline (PANI) were fabricated to capture muscle biopotential signals using electromyography (EMG). Changes in the pH of the AgNP solution during the synthesis process greatly influence the electrode’s performance. By analyzing the physicochemical characteristics of AgNP, measuring the electrical conductivity of each sample, and contrasting flexible electrode performance during EMG measurements, the impact of varying AgNP pH solutions will be investigated. According to the study, utilizing AgNP synthesized in the alkaline solution combined with PANI produces fabric-based flexible electrodes with a higher signal-to-noise ratio (SNR) and better conductivity than the standard Ag/AgCl commercial electrodes. Additionally, the alkaline solution-produced AgNP had a smaller particle size and greater crystallinity. During EMG monitoring, the absence of an electrolyte gel in the fabricated flexible electrode can provide comfort to the user without any risk of skin irritation from an electrolyte gel. The AgNP-PANI fabric electrode synthesized with AgNP in an alkaline solution has superior properties that make it potentially become a decent future commercial electrode for biopotential measurement.

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