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In the domain of field robotic system, it must deal with several challenges during both design and development durations. This study aims to investigate the whole design of robotic system for the internal inspection of tube. Firstly, the robot configuration is launched to identify the physical dimensions. Owing to the size of tube and the curved trajectory, the estimated parameters and shape of robot could be known. Also, some thresholds are obtained in this design which allows the range of dimensions. Secondly, the kinematic robot is analyzed to compute the flexible motion. Through theoretical computations, its locomotion could be evaluated directly. To realize our design, thirdly the detailed selection of servo motor is introduced in order to provide the powerful drive. Hence, the result of driving computations is shown due to the practical specifications of motor. Correspondingly, the spring which plays a role as damper, is measured to suppress the required torques. This spring ensures the continuous contact between leg of robot and internal wall of tube. As a result of these evaluations, both computer-based model and real-world model have been experimented. To guarantee the effectiveness and feasibility of our approach, three levels of validations are presented. In the numerical simulation, the virtual environment is established to substantiate the robotic locomotion. For the laboratory validation, the hardware robot is confirmed the working abilities as well as some trial results. Lastly, this robot must be exemplified in the real scene such as factory or site construction. From these achievements, it could be obviously seen that our approach is proper and feasible for many applications.

For many years, wheeled manipulator (WM) becomes an effective solution in many aspects of our economics such that it assists to maintain the highly manufacturing production. In this work, the proposed design of motion controller for WM to handle an object is introduced. The hardware of this WM consists of two components, mobile platform and robotic manipulator. Our concept is to design the structure of differential drive for mobile base while the configuration of on-board manipulator is the parallelogram mechanism. Owing to this architecture, the system modelling of whole body is then established to identify related constraints. To ensure the sustainable movement, the system state is asymptotically stable according to Lyapunov candidate function. Both numerical simulation and practical experiment are validated in various test scenarios. With 20 demonstrations for each task, the proposed approach reaches to success rate over 90% of test cases which well-track the desired trajectory and precisely complete the robotic manipulation.

Abstract In Industry 4.0, the innovative concept of a smart warehouse is defined as a progressive development in logistics and operations management. Numerous advanced techniques are recommended for integration, including wearable devices, communication, loading/unloading, optimization, robotics, and automated transportation, among others. Currently, the growing demand for swift cargo distribution, cost‐effective labour utilization, and enhanced productivity stands as an urgent topic for both enterprises and scholars. This paper presents an EtherCAT‐based model of automated guided vehicle (AGV)‐manipulation introduced within the Unity platform for smart warehouse management. The working environment selected for this model is a cross‐docking warehouse with a chessboard layout. Initially, theoretical computations pertaining to mechanical design are established to provide accurate parameters. Subsequently, the structure of the TwinCAT Human–Machine Interface Server and the configuration within the Unity environment are outlined. Following that, the software architecture, encompassing data exchange and a set of motion commands, is meticulously explained. To validate the proposed approach, several laboratory tests are conducted under identical conditions. This research offers several contributions: firstly, it introduces the innovative use of real‐time technology to represent AGV systems; secondly, it elucidates the potential connection between virtual reality and the physical model; and thirdly, it employs fluid motion techniques to illustrate and visualize driving performance. These findings hold significant promise not only for the advancement of smart warehouse management but also for the broader landscape of digital trade.

In this work, we study the ability to use hand gestures for human-machine interaction from wrist-worn sensors. Towards this goal, we design a wrist-worn prototype to capture RGB video stream of hand gestures. Then we built a new wrist-worn gesture dataset (named WiGes) with various subjects in interaction with home appliances in different environments. To the best of our knowledge, this is the first benchmark released for studying hand gestures from a wrist-worn camera. We then evaluate various CNN models for vision-based recognition. Furthermore, we deeply analyze the models that produce the best trade-off between accuracy, memory requirement, and computational cost. We point out that among studied architectures, MoviNet produces the highest accuracy. Then, we introduce a new MoviNet-based two-stream architecture that takes both RGB and optical flow into account. Our proposed architecture increases the Top-1 accuracy by 1.36% and 3.67% according to two evaluation protocols. Our dataset, baselines, and proposed model analysis give instructive recommendations for human-machine interaction using hand-held devices.

The global shipping industry has been contributing more than four-fifths of the volume of goods transported internationally. However, shipping is facing pressure from strict policies on combating climate change from the International Maritime Organization (IMO). Seaports are an essential component of shipping and are also having to change their development strategies to be more sustainable and smarter. The concept of a green port was born as an inevitable part of the green economic development path. Green port system development policies and strategies have been discussed and studied in recent literature reviews, and have revealed the limited reach of developing countries. This work focuses on evaluating successful green port policies and concepts in developed countries to highlight the distinctive features of green ports. Moreover, the experiences from the effective green port models of developed countries can be valuable lessons for developing countries like Vietnam. More interestingly, the core features of green ports, when piloted at several international ports in Vietnam, promote port efficiency and environmental friendliness. On the way to implementing the national green port strategy, Vietnam has been establishing legal and infrastructure corridors to realize the strategy of sustainable marine economic development by 2045.

In this article, we propose the visual application of a navigation framework for a wheeled robot to disinfect surfaces. Since dynamic environments are complicated, advanced sensors are integrated into the hardware platform to enhance the navigation task. The 2D lidar UTM-30LX from Hokuyo attached to the front of the robot can cover a wide scanning area. To provide better results in laser scan matching, an inertial measurement unit was integrated into the robot’s body. The output of this combination feeds into a global costmap for monitoring and navigation. Additionally, incremental encoders that obtain high-resolution position data are connected to the rear wheels. The role of the positioning sensor is to identify the existing location of the robot in a local costmap. To detect the appearance of a human, a Kinect digital camera is fixed to the top of the robot. All feedback signals are combined in the host computer to navigate the autonomous robot. For disinfection missions, the robot must carry several ultraviolet lamps to autonomously patrol in unknown environments. To visualize the robot’s effectiveness, our approach was validated using both a virtual simulation and an experimental test. The contributions of this work are summarized as follows: (i) a structure for ultraviolet-based hardware was first established; (ii) the theoretical computations for the robot’s localization in the 3D workspace will play a fundamental role in further developments; and (iii) data fusion from advanced sensing devices was integrated to enable navigation in uncertain environments.

The low thermal conductivity of phase change materials (PCMs) is a crucial challenge in utilizing latent heat thermal energy storage (LHTES) systems. Incorporating fins into LHTES system is an effective approach to overcoming the low thermal conductivity of PCM and enhancing its performance. In the present study, computational fluid dynamics is used to investigate the effects of fin configurations and operating conditions on the performance of a shell-and-tube system assisted by fins. The heat transfer rate and liquid fraction are investigated to evaluate the thermal behavior of the proposed system. The enthalpy–porosity technique is employed to simulate the phase change. The temperature variations over time at different PCM locations are calculated and compared with the measured temperatures. The predicted results show that the fin thickness and the inlet temperature of the heat transfer fluid play a key role in reducing the melting and solidification time. The obtained results indicated that by increasing the fin thickness from 0.5 to 1 mm, the heat transfer rate increased by approximately 17%. Also, increasing the inlet temperature from 60 °C to 65 °C improved the heat transfer rate by 36.2%.

Representation of dynamic textures (DTs), well‐known as a sequence of moving textures, is a challenging problem in video analysis due to the disorientation of motion features. Analysing DTs to make them ‘understandable’ plays an important role in different applications of computer vision. In this study, an efficient approach for DT description is proposed by addressing the following novel concepts. First, the beneficial properties of dense trajectories are exploited for the first time to efficiently describe DTs instead of the whole video. Second, two substantial extensions of local vector pattern operator are introduced to form a completed model which is based on complemented components to enhance its performance in encoding directional features of motion points in a trajectory. Finally, the authors present a new framework, called directional dense trajectory patterns, which takes advantage of directional beams of dense trajectories along with spatio‐temporal features of their motion points in order to construct dense‐trajectory‐based descriptors with more robustness. Evaluations of DT recognition on different benchmark datasets (i.e. UCLA, DynTex, and DynTex++) have verified the interest of the authors’ proposal.

Polymer-based micro-optical components are very important for applications in optical communication. In this study, we theoretically investigated the coupling of polymeric waveguide and microring structures and experimentally demonstrated an efficient fabrication method to realize these structures on demand. First, the structures were designed and simulated using the FDTD method. The optical mode and loss in the coupling structures were calculated, thereby giving the optimal distance for optical mode coupling between two rib waveguide structures or for optical mode coupling in a microring resonance structure. Simulations results then guided us in the fabrication of the desired ring resonance microstructures using a robust and flexible direct laser writing technique. The entire optical system was thus designed and manufactured on a flat base plate so that it could be easily integrated in optical circuits.

Human contact is a key issue in social interactions for autonomous systems since robots are increasingly appearing everywhere, which has led to a higher risk of conflict. Particularly in the real world, collisions between humans and machines may result in catastrophic accidents or damaged goods. In this paper, a novel stop strategy related to autonomous systems is proposed. This control method can eliminate the vibrations produced by a system’s movement by analysing the poles and zeros in the model of autonomous vehicles and goods. Using the pole placement technique, the motion of a system is guaranteed to be more stable, more flexible and smoother. Moreover, several control profiles are employed in the switching mechanism to choose the proper vibration-free effect. The main contributions of this paper are (i) the recommendation of an active stopping planner using different smooth generators from a modelling study, (ii) the validation of their physical characteristics and (iii) the launching of a switching algorithm based on the socially aware navigation framework of a robot. This theoretical work is based on the virtual environment of MATLAB, and the experiment is implemented in the practical platform of an automated guided vehicle. From these results, it can be seen that the proposed approach is robust, effective and feasible for applications in storehouse management, public transportation or factory manufacturing.

Targeted radionuclide therapy (TRT) and immunotherapy are rapidly growing classes of cancer treatments. Basic, translational, and clinical research are now investigating therapeutic combinations of these agents. In comparison to external beam radiation therapy (EBRT), TRT has the unique advantage of treating all disease sites following intravenous injection and selective tumor uptake and retention—a particularly beneficial property in metastatic disease settings. The therapeutic value of combining radiation therapy with immune checkpoint blockade to treat metastases has been demonstrated in preclinical studies, whereas results of clinical studies have been mixed. Several clinical trials combining TRT and immune checkpoint blockade have been initiated based on preclinical studies combining these with EBRT and/or TRT. Despite the interest in translation of TRT and immunotherapy combinations, many questions remain surrounding the mechanisms of interaction and the optimal approach to clinical implementation of these combinations. This review highlights the mechanisms of interaction between anti-tumor immunity and radiation therapy and the status of basic and translational research and clinical trials investigating combinations of TRT and immunotherapies.

In previous works, the perceived safety and comfort are currently not the principal objectives of all industries, especially robotics system. It might lead not to take psychological safety into consideration of adjusting robot behavior, hence, the human-robot interaction lacks of ease and naturalness. In this paper, a novel framework of human’s zones to ensure safety for social interactions in human-machine system is proposed. In the context of service robot in hospital, machine should not produce any actions that may induce worry, surprise or bother. To maintain the comfortable interaction, an algorithm to update human’s state into personal space is developed. Then, a motion model of robot is demonstrated with assumption of the reference path under segmentation. Dynamic Window Approach is employed for motion planning while Optimize Cost function searches the shortest path in a graph. To validate our approach, three test cases (without human-aware framework, with basic model of human’s zone and with extended personal space) are carried out in the same context. Moreover, three interactive indicators, for instance collision index (CI), interaction index (CII) and relative velocity of robot (Vr), are analyzed in different situations. Lack of human-aware framework, robot might break all thresholds and meet the potential collisions. While robot with basic model of human’s zone in its perception maintains the physically safe thresholds but not socially, it respects whole criterions in both physical constraints and social relations. As a result, our findings are useful for robot’s navigation in presence of human while the socially comfortable interaction is guaranteed.

We have proposed a fruitful design principle targeting a concentration ratio (CR) >1000× for a typical high concentrating photovoltaics (HCPV) system, on account of a two-concentrator system + homogenizer. The principle of a primary dual-lens concentrator unit, completely analogous basic optics seen in the superposition compound eyes, is a trend not hitherto reported for solar concentrators to our knowledge. Such a concentrator unit, consisting of two aspherical lenses, can be applied to minify the sunlight and reveal useful effects. We underline that, at this stage, the CR can be attained by two orders of magnitude simply by varying the radius ratio of such two lenses known from the optics side. The output beam is spatially minimized and nearly parallel, exactly as occurs in the superposition compound eye. In our scheme, thanks to such an array of dual-lens design, a sequence of equidistant focal points is formed. The secondary concentrator consists of a multi-reflective channel, which can collect all concentrated beams from the primary concentrator to a small area where a solar cell is placed. The secondary concentrator is located right underneath the primary concentrator. The optical characteristics are substantiated by optical simulations that confirm the applicability of thousands-fold gain in CR value, ~1100×. This, however, also reduced the uniformity of the illumination area. To regain the uniformity, we devise a fully new homogenizer, hinging on the scattering principle. A calculated optical efficiency for the entire system is ~75%. Experimentally, a prototype of such a dual-lens concentrator is implemented to evaluate the converging features. As a final note, we mention that the approach may be extended to implement an even higher CR, be it simply by taking an extra concentrator unit. With simple design of the concentrator part, which may allow the fabrication process by modeling method and large acceptant angle (0.6°), we assess its large potential as part of a general strategy to implement a highly efficient CPV system, with minimal critical elaboration steps and large flexibility.

Pendubot is a classical under-actuated SIMO model for control algorithm testing in laboratory of universities. In this paper, authors design a fuzzy-sliding control for this system. The controller is designed from a new idea of application of fuzzy algorithm for optioning control parameters. The response of system on TOP position under fuzzysliding control algorithm is proved to be better than under sliding controller through Matlab/Simulink simulation.

Integration of distributed generators (DGs) into a distribution network (DN) can cause coordination challenges of overcurrent relays (OCRs) because of different fault-current contributions of DGs as well as the directional change in fault currents. Therefore, the OCRs should be properly coordinated to maintain their adaptability and scalability to protect the DG-integrated distribution network. In this study, an adaptive and scalable protection coordination (ASPC) approach has been developed for the OCRs in a DG-contained distribution network based on two implementation stages. At the first stage, the reliability improvement of fault-current calculation results is performed by determining the min-max confidence interval of fault current for each different fault type, which is the basis for properly selecting tripping and pick-up thresholds of definite-time and inverse-time OC functions in the same OCR. At the second stage, optimization algorithms are used for calculating protection-curve coefficients and Time-Dial Setting (TDS) multiplier for the inverse-time OC functions in the OCR. A real 22 kV DG-integrated distribution network which is simulated by ETAP software is considered a reliable test-bed to validate the proposed ASPC system of OCRs in the multiple-DG-contained distribution network. In addition, the coordination results of OCRs can be obtained by three common optimization algorithms, Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), and Genetic Algorithm (GA). These relay coordination results have shown an effective protection combination of the definite-time OC functions (50P and 50G) and the inverse-time OC functions (51P and 51G) in the same OCR to get the adaptable and scalable DN protection system.

Currently, integrated trends play a key role in every aspect of automation applications. In particular, if the mechanization of agriculture becomes a competitive factor among farmers or nations, then the multi-functional transportation of agricultural products is inevitable in global trade. In sustainable transportation, the challenge of overcoming stable control in harsh environments, such as through imprecise parameters or varying loads, should be addressed. In this paper, a novel controller for a nonholonomic mechanical system able to adapt to uncertainties is proposed. Based on the multi-functional autonomous carrier (MAC), the system configuration of the kinematic and dynamic model is launched in order to identify the unstable problems that arise when tracking the trajectory. To solve these troubles, the decoupled formation of a MAC system has been investigated by considering two second-order components, namely a linear speed-based sub-system and angular speed-based sub-system. To stabilize the whole system using the Lyapunov theory, the advanced control techniques are studied. To validate the proposed approach, a series of test scenarios have been carried out. From the superior performance of numerous trials, it is clear that our approach is effective, feasible, and reasonable for the advanced control of agricultural applications.

To work in shared space with humans, autonomous systems must carry unknown loads in predefined missions. With the conventional control scheme, the grounded robot would suffer unstable motion and imprecise tracking performance. To overcome these challenges, in this paper, a novel controller using an adaptive sliding mode for autonomous grounded robots (AGR) is proposed. This control strategy takes into consideration uncertain characteristics, varying loads, and external disturbances. To analyze the tracking performance precisely, the overall error of motion system is decoupled into two subsystems where the second-order system is related to the angular tracking error and the third-order system is associated with the linear one. Initially, the dynamics model of the grounded robot is established containing different elements of nonlinear forces in order to address the technical problems. Then, the system state equation of the autonomous system is mentioned to indicate the theoretical characteristics. Based on the proposed controller, the stability of the system is validated by the Lyapunov theorem. From the results of numerical tests, three practical situations consisting of separately linear and circular trajectories with varying loads and an S-curve trajectory of a working map are suggested. The tracking performance validates that the proposed control scheme is, in various scenarios, robust, effective, and feasible. From these superior outcomes, it can be determined obviously the property of our works in accommodating the variations of cargo from applications in distribution centers, material transportation, or handling equipment.

The supervision and feeding of grazing livestock are always difficult missions. Since animals act based on habits, the real-time monitoring data logger has become an indispensable instrument to assist farmers in recognizing the status of livestock. Position-tracked and acoustic monitoring have become commonplace as two of the best methods to characterize feeding performance in ruminants. Previously, the existing methods were limited to desktop computers and lacked a sound-collecting function. These restrictions impacted the late interventions from feeders and required a large-sized data memory. In this work, an open-source framework for a data collector that autonomously captures the health information of farm animals is introduced. In this portable hardware, a Wireless Location Acoustic Sensing System (WiLASS) is integrated to infer the health status through the activities and abnormal phenomena of farming livestock via chew–bite sound identification. WiLASS involves the open modules of ESP32-WROOM, GPS NEO-6M, ADXL335 accelerometer, GY-MAX4466 amplifier, temperature sensors, and other signal processing circuits. By means of wireless communication, the ESP32-WROOM Thing micro-processor offers high speed transmission, standard protocol, and low power consumption. Data are transferred in a real-time manner from the attached sensing modules to a digital server for further analysis. The module of GPS NEO-6M Thing brings about fast tracking, high precision, and a strong signal, which is suitable for highland applications. Some computations are incorporated into the accelerometer to estimate directional movement and vibration. The GY-MAX4466 Thing plays the role of microphone, which is used to store environmental sound. To ensure the quality of auditory data, they are recorded at a minimum sampling frequency of 10 KHz and at a 12-bit resolution. Moreover, a mobile software in pocket devices is implemented to provide extended mobility and social convenience. Converging with a cloud-based server, the multi-Thing portable platform can provide access to simultaneously supervise. Message Queuing Telemetry Transport (MQTT) protocol with low bandwidth, high reliability, and bi-direction, and which is appropriate for most operating systemsOS, is embedded into the system to prevent data loss. From the experimental results, the feasibility, effectiveness, and correctness of our approach are verified. Under the changes of climate, the proposed framework not only supports the improvement of farming techniques, but also provides a high-quality alternative for poor rural areas because of its low cost and its ability to carry out a proper policy for each species.

The short‐term load prediction is the critical operation in the peak demand administration and power generation scheduling of buildings that integrated the smart solar microgrid (SSM). Many research studies have proved that hybrid deep learning strategies achieve more accuracy and feasibility in practical applications than individual algorithms. Moreover, many buildings have integrated the SSM on the rooftop with the battery management system (BMS) to enhance energy efficiency management. However, the traditional methodologies only processed the weather parameters and power demand information for short‐term load prediction, ignoring the collected data from SSM and BMS by the advanced metering infrastructures (AMI), which probably improved prediction accuracy. In this research, many accumulated data of building and SSM are collected before methodology implementation. Considering the diversities of accumulated parameters from SSM and BMS, an adaptive convolution neural network long short‐term memory (CNN‐LSTM) is proposed for hourly electrical load prediction. The CNN could extract the critical large‐scale input feature, while the LSTM could achieve better accurate forecasts. The Pearson correlation matrix is calculated for the feature selection scheme from different data units. The hyperparameter tuning is utilized for obtaining the optimized hybrid CNN‐LSTM algorithm. The K‐fold cross‐validation is employed for deep learning algorithm verification, which includes LSTM, GRU, CNN, and Bi‐LSTM methodologies. The results prove that the hybrid CNN‐LSTM achieved outperformed improvements, which are 20.57%, 29.63%, 19.06% in MSE, MAE, MAPE, and 21.24%, 22.02%, 3.82% in validating MSE, MAE, MAPE, respectively. The hybrid CNN and LSTM combined with the feature selection scheme achieve superior predicting accuracies, proving the adaptability ability for integrating into the energy management system (EMS) of the building's SSM. [ABSTRACT FROM AUTHOR]

Epithelia must be able to resist mechanical force to preserve tissue integrity. While intercellular junctions are known to be important for the mechanical resistance of epithelia, the roles of tight junctions (TJs) remain to be established. We previously demonstrated that epithelial cells devoid of the TJ membrane proteins claudins and JAM-A completely lack TJs and exhibit focal breakages of their apical junctions. Here, we demonstrate that apical junctions fracture when claudin/JAM-A-deficient cells undergo spontaneous cell stretching. The junction fracture was accompanied by actin disorganization, and actin polymerization was required for apical junction integrity in the claudin/JAM-A-deficient cells. Further deletion of CAR resulted in the disruption of ZO-1 molecule ordering at cell junctions, accompanied by severe defects in apical junction integrity. These results demonstrate that TJ membrane proteins regulate the mechanical resistance of the apical junctional complex in epithelial cells. [ABSTRACT FROM AUTHOR]

Representing dynamic textures (DTs) plays an important role in many real implementations in the computer vision community. Due to the turbulent and non-directional motions of DTs along with the negative impacts of different factors (e.g., environmental changes, noise, illumination, etc.), efficiently analyzing DTs has raised considerable challenges for the state-of-the-art approaches. For 20 years, many different techniques have been introduced to handle the above well-known issues for enhancing the performance. Those methods have shown valuable contributions, but the problems have been incompletely dealt with, particularly recognizing DTs on large-scale datasets. In this article, we present a comprehensive taxonomy of DT representation in order to purposefully give a thorough overview of the existing methods along with overall evaluations of their obtained performances. Accordingly, we arrange the methods into six canonical categories. Each of them is then taken in a brief presentation of its principal methodology stream and various related variants. The effectiveness levels of the state-of-the-art methods are then investigated and thoroughly discussed with respect to quantitative and qualitative evaluations in classifying DTs on benchmark datasets. Finally, we point out several potential applications and the remaining challenges that should be addressed in further directions. In comparison with two existing shallow DT surveys (i.e., the first one is out of date as it was made in 2005, while the newer one (published in 2016) is an inadequate overview), we believe that our proposed comprehensive taxonomy not only provides a better view of DT representation for the target readers but also stimulates future research activities. [ABSTRACT FROM AUTHOR]