Abstract Autonomous fabric manipulation is a challenging task due to complex dynamics and potential self-occlusion during fabric handling. An intuitive method of fabric-folding manipulation first involves obtaining a smooth and unfolded fabric configuration before the folding process begins. However, the combination of quasi-static actions like pick & place and dynamic action like fling proves inadequate in effectively unfolding long-sleeved T-shirts with sleeves mostly tucked inside the garment. To address this limitation, this paper introduces an enhanced quasi-static action called pick & drag, specifically designed to handle this type of fabric configuration. Additionally, an efficient dual-arm manipulation system is designed in this paper, which combines quasi-static (including pick & place and pick & drag) and dynamic fling actions to flexibly manipulate fabrics into unfolded and smooth configurations. Subsequently, once it is confirmed that the fabric is sufficiently unfolded and all fabric keypoints are detected, the keypoint-based heuristic folding algorithm is employed for the fabric-folding process. To address the scarcity of publicly available keypoint detection datasets for real fabric, we gathered images of various fabric configurations and types in real scenes to create a comprehensive keypoint dataset for fabric folding. This dataset aims to enhance the success rate of keypoint detection. Moreover, we evaluate the effectiveness of our proposed system in real-world settings, where it consistently and reliably unfolds and folds various types of fabrics, including challenging situations such as long-sleeved T-shirts with most parts of sleeves tucked inside the garment. Specifically, our method achieves a coverage rate of 0.822 and a success rate of 0.88 for long-sleeved T-shirts folding. Supplemental materials and dataset are available on our project webpage at https://sites.google.com/view/fabricfolding.

This paper presents a novel approach for monitoring social media content related to Airbnb reviews, explicitly focusing on the sentiments expressed by immigrants in the United Kingdom. The proposed system, a Quick Search System, leverages machine learning techniques to perform sentiment analysis on many Airbnb reviews. The system aims to provide timely and insightful information about the experiences and sentiments of immigrants in the UK, as reflected in their Airbnb reviews. By employing state-of-the-art machine learning algorithms, the system enables efficient and accurate sentiment classification, allowing for the identification of key themes and sentiments expressed by immigrant users. The study demonstrates the potential of this approach in gaining a deeper understanding of immigrant perspectives within the context of peer-to-peer accommodation, and its implications for social media monitoring and customer satisfaction management.This present study has conducted a critical analysis utilizing efficient feature extraction techniques, including N-grams and TF-IDF, to optimize identifying positive, neutral, and negative feedback. Furthermore, five different models were utilized, and the training and testing processes were accompanied by parameter tuning. Ultimately, the study concluded that the Random Forest (RF) classifier performed exceptionally well, achieving a 95% accuracy rate.

Numerical simulation and experimental investigation on Phase Change Materials based energy storage system for cooling the water in process industries towards water conservation and environmental sustainability

An automated defense robot building is planned, that has a laser gun attached, which is utilized for pointing laser rays to destroy the target object. One of the most important things about these robots is that they have the capability to perform missions remotely in the field, without any actual danger to human lives. In the proposed system, a robot is developed that is controlled through Microcontroller. For the aiming purpose and to view the road and the surroundings in which the robot is travelling, wireless cameras are installed. Radio frequency can be used to control the robot. These defense robots used in military are usually employed with the integrated system including gripper, cameras and sensors. This is specially designed for defense robotic system to save human life and protect the country from enemies. In the existing systems, personal computer using ZigBee protocol is used to monitor the robot.

Waste oxy-fuel combustion integrated with supercritical CO2 cycle and high-temperature electrolysis technologies for e-methanol production: A feasibility analysis

This paper presents a novel voice-controlled restaurant robot system designed to revolutionize the dining experience by seamlessly integrating advanced robotics and natural language processing technologies. The proposed system aims to enhance customer service, operational efficiency, and overall dining satisfaction by providing a user-friendly and interactive platform. .The restaurant robot is equipped with a comprehensive set of capabilities, including, voice recognition and manipulation. By leveraging voice control, users can interact with the robot using natural language commands, enabling seamless communication and intuitive control. This allows customers to effortlessly place orders The system's is comprises a mobile robotic platform, bluetooth modules, a cloud-based natural language processing, The voice control functionality is implemented through a sophisticated speech recognition system, enabling the robot to accurately understand and interpret user commands. .In addition to customer-facing interactions, the restaurant robot also assists restaurant staff in various tasks. Deliver food and beverages to tables, handle payment transactions, and perform routine cleaning tasks. By automating these processes, the system improves operational efficiency, reduces labor costs, and allows human staff to focus on highervalue activities such as customer engagement and culinary expertise. .To evaluate the performance and usability of the voice-controlled restaurant robot, a series of experiments were conducted in a real-world restaurant environment. The results demonstrated the system's high recognition accuracy, robustness in noisy environments, and its ability to effectively handle a wide range of user requests. .The proposed voice-controlled restaurant robot presents a promising solution for transforming the dining experience. It combines the convenience of voice control with advanced robotics, enabling seamless and interactive interactions between customers, staff, and the robotic platform. The system's ability to enhance customer service, operational efficiency, and overall dining satisfaction positions it as a significant advancement in the hospitality industry.

The world on the beginning of a new period of regeneration and developing with the increase of Internet and also Internet of Thing (IoT) in the market commercial application. where a new revolution can be made by characterizing its important. The rapprochement of the universal system and the power of sophisticated calculating, sensing with low cost and add create of communication supported by the Internet. In this paper, the green house system is consisting of two sections they are the software and hardware sections. Hardware has three types of sensors for natural environment measuring and gateway (router). Software consists of monitoring and controlling sub system, and IoT cloud, vb.Net software is used to write the programs codes for the proposed system. The environmental parameters of the greenhouse are continuously recorded by various sensors, and the collected data is displayed on a customized website. The system software has the ability to monitor and control the green house environment variables like humidity, temperature and water level on the greenhouse. Thus, the greenhouse can be monitored and controlling from anywhere and at any time. The fundamental aims of this paper have been achieved with the help of all systems parts. The green house environment has been displayed using the IoT cloud and the control is done over the internet connection. The system is implemented in practices. The system result gives an accurate indication that the system is reliable and work completely.

In the contemporary world, science and technology are advancing swiftly to meet the growing human need for electricity. Within this framework, street lighting, serving as the most vital and ubiquitous element of urban lighting infrastructure, contributes significantly to public electricity consumption. Hence, enhancing the operational efficiency of street lamps becomes imperative to conserve energy. Nonetheless, traditional street lighting systems, being manually controlled, consuming excessive power, and entailing high installation expenses, present notable drawbacks and concerns. Leveraging the internet of things (IoT), advanced innovations automate various areas, including health monitoring, traffic management, agricultural irrigation, street lights, and classrooms. The current manual operation of street lights leads to substantial global energy waste. To address this, an integrated hardware and software solution is proposed. Practical implementation of the hardware devices employs a wireless sensor network, while the software focuses on developing an IoT application for data storage, analysis, and visualization. The proposed system enables effective monitoring of parameters such as ambient temperature, current, voltage, and energy consumption of photovoltaic street lights, which are used as an indicator of the lamp status. Using Xbee modules, a configuration by the X-CTU software is necessary to communicate between all street lights wirelessly. These Xbee modules are used as a leading technology for wireless sensor networks due to its low power and low cost. Experimental results demonstrate that the proposed system is energy-efficient and cost-effective, and further can be implemented in real street light systems.

Permanent magnet synchronous motor (PMSM) is widely used in different applications because of their different key operation features. But despite having important key features, PMSM suffers from torque ripple when simple control techniques like direct torque control (DTC) are employed. Several approaches have been raised by many researchers to lessen the ripple when DTC is used for controlling the PMSM drive. In this work, duty ratio-optimized DTC control, which has the same simplicity as conventional DTC (CDTC), and delay compensation for input signal prediction are proposed. In the proposed system, to determine the duty ratio, slope manipulation was made by considering the minimization of torque error and forcing the developed torque to be equal to the reference torque at the end of the span. An online slope determination approach was employed to compute the duty ratio. MATLAB 2021b is used for simulation purposes. The dynamics and steady-state performance of the proposed scheme were tested for both variable-load and variable-speed operations. In addition, the harmonic performance and ratio of harmonic power loss to total active power loss of the motor were evaluated. In general, the proposed scheme performs effectively in reducing steady-state ripple and harmonic.

Interconnection is a channel that links multiple water channel systems with those downstream of a river area. The proposed system will utilize existing canals in Gadungan, Matan, and Unun Irrigation Areas, extending to the Lambuk Reservoir dam system. The problem is that irrigation areas downstream of the Lambuk Reservoir are Lambuk (726 ha), Babakan Anyar (42 ha), and Lanyah (281 ha) experience water shortages (planting intensity of 170% to 200%) during the dry season. The study utilized water balance methods in rice fields, the basic year method for main discharge at weirs, and interconnection channel analysis for water needs calculation. From the water balance analysis, the Singin System cannot make a new interconnection channel to the Lambuk Reservoir because the available water in each reservoir can only irrigate the Matan and Unun Irrigation Areas. From the Telaga Tunjung System, there is still a lot of water available, especially during the rainy season, so that it can supply the reservoir for the Lambuk Reservoir. This means that the interconnection system from this reservoir still needs to be created from the primary channel of the Gadungan Irrigation Area.

Broadband active noise control systems incorporating fixed controllers exhibit limited ability to reduce sinusoids. This study presents a semi-adaptive feedforward hybrid active noise control (HANC) system to address this issue. The proposed system pairs fixed high-order optimal controllers for broadband noise with adaptive low-order FXLMS-based controllers for narrowband noise. Notably, parallel broadband and narrowband controllers work independently. The proposed semi-adaptive feedforward HANC system demonstrates low computational complexity which makes it suitable for multichannel systems. Simulations and experiments validate the effectiveness of the proposed system in controlling mixed noise.

During the COVID-19 pandemic, there was an increasing need to expand diagnostic testing in hospitals. At Keio University Hospital (KUH), clinical staff were concerned that the demand for PCR testing might exceed the capacity of the Clinical Laboratory. In response, basic researchers at Keio University School of Medicine (KUSM) set out to build a new, collaborative, PCR testing system. To be authorized to perform such diagnostic PCR testing, KUSM registered its core laboratory as an external clinical laboratory (ECL). In the pandemic, there was a pressure to build the PCR system quickly. Speed required discussions that developed a shared understanding of the unprecedented, new KUH/KUSM PCR system. To design, construct, and archive the new PCR testing system, we used a systems engineering (SE) approach. This included diagram visualization of functional flows and application of the Unified Architecture Framework (UAF), both of which are often used in system building. We considered daily demand for PCR testing at KUH and KUSM, and daily COVID-19 infections in Japan. We operated the collaborative PCR testing system from August 2020 to June 2022. Given public health insurance reimbursement policies, KUH focused on individuals with suspicious symptoms, while the ECL at KUSM screened samples from asymptomatic individuals. KUSM performed about half as many tests as KUH. Interviewing KUH staff revealed that diagrams helped promote a better understanding of the KUH/KUSM PCR testing system. When designing temporary systems that may be repurposed in the future, we suggest using an SE approach with diagrams and UAF perspectives. This approach will enable stakeholders to understand what is being proposed to be built, and facilitate achieving an informed consensus on the proposed system. We suggest that SE approaches should be widely used in projects that involve building and operating complex, collaborative systems, and documenting the process.

The modelling and performance analysis of free body dynamics in electric vehicles (EVs) plays a crucial role in understanding and optimizing the vehicle's behavior and performance. This research focuses on accurately modelling the forces and motions acting on the vehicle body, excluding the powertrain components. By considering factors such as vehicle weight, suspension characteristics, tire properties, and aerodynamic forces, a comprehensive mathematical model is developed. This model enables the simulation and analysis of the vehicle's behavior under various operating conditions. The performance analysis involves evaluating key metrics such as vehicle response, stability limits, and ride comfort. The proposed system is compared with the existing electric vehicle in the market. The findings from this research contribute to the design and development of EVs with improved handling, stability, and energy efficiency. Additionally, they inform the development of advanced driver-assistance systems (ADAS) and autonomous driving technologies. Overall, the modelling and performance analysis of free body dynamics in EVs supports the advancement of sustainable and efficient transportation systems.

Cochlear implant (CI) is currently the vital technological device for assisting deaf patients in hearing sounds and greatly enhances their sound listening appreciation. Unfortunately, it performs poorly for music listening because of the insufficient number of electrodes and inaccurate identification of music features. Therefore, this study applied source separation technology with a self-adjustment function to enhance the music listening benefits for CI users. In the objective analysis method, this study showed that the results of the source-to-distortion, source-to-interference, and source-to-artifact ratios were 4.88, 5.92, and 15.28 dB, respectively, and significantly better than the Demucs baseline model. For the subjective analysis method, it scored higher than the traditional baseline method VIR6 (vocal to instrument ratio, 6 dB) by approximately 28.1 and 26.4 (out of 100) in the multi-stimulus test with hidden reference and anchor test, respectively. The experimental results showed that the proposed method can benefit CI users in identifying music in a live concert, and the personal self-fitting signal separation method had better results than any other default baselines (vocal to instrument ratio of 6 dB or vocal to instrument ratio of 0 dB) did. This finding suggests that the proposed system is a potential method for enhancing the music listening benefits for CI users.

Preconcentrating sensor systems toward indoor low-concentration VOC detection by goal-oriented, sequential, inverse design strategy

In this article, we propose a novel fire drill training system designed specifically to integrate augmented reality (AR) and virtual reality (VR) technologies into a single head-mounted display device to provide realistic as well as safe and diverse experiences. Applying hybrid AR/VR technologies in fire drill training may be beneficial because they can overcome limitations such as space-time constraints, risk factors, training costs, and difficulties in real environments. The proposed system can improve training effectiveness by transforming arbitrary real spaces into real-time, realistic virtual fire situations, and by interacting with tangible training props. Moreover, the system can create intelligent and realistic fire effects in AR by estimating not only the object type but also its physical properties. Our user studies demonstrated the potential of integrated AR/VR for improving training and education.

The global agriculture system faces significant challenges in meeting the growing demand for food production, particularly given projections that the world's population will reach 70% by 2050. Hydroponic farming is an increasingly popular technique in this field, offering a promising solution to these challenges. This paper will present the improvement of the current traditional hydroponic method by providing a system that can be used to monitor and control the important element in order to help the plant grow up smoothly. This proposed system is quite efficient and user-friendly that can be used by anyone. This is a combination of a traditional hydroponic system, an automatic control system and a smartphone. The primary objective is to develop a smart system capable of monitoring and controlling potential hydrogen (pH) levels, a key factor that affects hydroponic plant growth. Ultimately, this paper offers an alternative approach to address the challenges of the existing agricultural system and promote the production of clean, disease-free, and healthy food for a better future.

Object-aware interactive perception for tabletop scene exploration

<p>Sign language recognition plays a crucial role in<br />facilitating communication for individuals with hearing<br />impairments. This paper presents a deep learning-based<br />approach for recognizing Bahasa Isyarat Indonesia (BISINDO),<br />the sign language used in Indonesia. The proposed system<br />employs convolutional neural networks (CNNs) and recurrent<br />neural networks (RNNs) to automatically extract features from<br />sign language gestures and classify them into corresponding<br />linguistic units. The dataset used for training and evaluation<br />consists of annotated BISINDO sign language videos.<br />Preprocessing techniques such as normalization and<br />augmentation are applied to enhance the robustness of the<br />model. Experimental results demonstrate the effectiveness of the<br />proposed approach in accurately recognizing BISINDO sign<br />language gestures, achieving state-of-the-art performance<br />compared to existing methods. The developed system shows<br />promising potential for real-world applications in enhancing<br />communication accessibility for the hearing-impaired<br />community in Indonesia.</p>

Solar power systems in homes have become the need of the hour due to the crisis of fossil fuels. Also, it is a useful way of rural electrification and cutting down on running electricity costs. This paper discusses a 26-kW solar power system for powering homes along with IoT-based monitoring. The proposed system is expected to be low in cost and highly efficient. The system can also be used as a battery backup without solar power. The emergence of Blockchain technology is poised to revolutionize the sharing of information by providing a means of building trust in decentralized settings without the reliance on intermediaries. This technological breakthrough has the potential to transform several industries, including the internet of things (IoT). In addition to Blockchain, IoT has also been able to address some of its limitations by utilizing innovative technologies like big data and cloud computing. For security, Blockchain as a decentralized application will be used. Each block typically contains the transaction data, and power consumption data which can’t be tampered with even if changing all subsequent blocks, which is expensive to do so.