Volume 14, Issue 1

One of the key questions in human–robot interaction research is whether humans perceive robots as intentional agents, or rather only as mindless machines. Research has shown that, in some contexts, people do perceive robots as intentional agents. However, the role of prior exposure to robots as a factor potentially playing a role in the attribution of intentionality is still poorly understood. To this end, we asked two samples of high school students, which differed with respect to the type of education they were pursuing (scientific/technical vs. artistic) to complete the InStance Test, measuring individual tendency to attribute intentionality toward robots. Results showed that, overall, participants were more prone to attribute intentionality to robots after being exposed to a theoretical lecture about robots’ functionality and use. Moreover, participants’ scientific/technical education resulted in a higher likelihood of attribution of intentionality to robots, relative to those with artistic education. Therefore, we suggest that the type of education, as well as individually acquired knowledge, modulates the likelihood of attributing intentionality toward robots.

A growing number of studies investigate the use of robotics therapy for motor (re)habilitation with children with cerebral palsy (CP). Most of these studies use functional robots in very repetitive sessions. While the therapy is effective, very few studies employ social robots, which appears to be a missed opportunity to design more compelling and enjoyable sessions for the children. In this article, we will review robot-assisted upper limb motor (re)habilitation for children with CP. Previous reviews of robot-assisted therapy for CP had mostly focused on lower limbs, or the review was made from a medical point of view, with the sole concern being the therapy’s effectiveness. Here, we focus our review on robot-assisted upper limb (re)habilitation and address human–robot interaction considerations. We searched PubMed, Scopus, and IEEE databases and argue that although this area of research is promising and already effective, it would benefit from the inclusion of social robots for a more engaging and enjoyable experience. We suggest four scenarios that could be developed in this direction. The goal of this article is to highlight the relevance of the past work and encourage the development of new ideas where therapy will socially engage and motivate children.

Patient education is a crucial part of the efforts to improve the level of medication adherence. Successful diabetes self-management will be achieved if health practitioners (doctors, pharmacists, nurses, and other health-related professionals) provide education to their patients. One of the multimedia platforms that are now popular in patient education and health promotion is video. This study primarily aimed to review published research examining integration of video-based digital interventions into tech-based patient education methods to promote medication adherence in diabetic patients. All of the articles presented in this review were collected from the PubMed, Science Direct, and Sage Publication databases. This review compared study samples, intervention characteristics (length of intervention and learning media), and adherence outcomes, as well as additional outcomes across the studies. Of the 304 articles reviewed, seven studies that met the criteria were found and included in the review. Overall, 57.14% of the integrated video-based interventions demonstrated improved medication adherence and 42.86% demonstrated improvement in other related health outcomes. The interventions were 7 weeks to 6 months in length and were in the form of storytelling with videos, smartphone apps, educational programs, or web-based education. It can be concluded that video-based digital interventions aimed to improve medication adherence in diabetic patients are promising. For optimum outcomes, this study highly suggests that a combination of video (synchronous and asynchronous) and a good patient education program is an ideal form of intervention for patients with type 2 diabetes. Regarding the education platform, the intervention could be humanoid robots, storytelling, mobile health (mHealth) apps, websites, robot-enhanced diabetes care, and other related tech-based programs or even conventional models.

The Internet of things (IoT) is a technology with varied applications in numerous fields. One such field is healthcare, which has a dire need of using this technology to help millions benefit from the attention and availability of healthcare professionals. This study identifies important factors that influence the adoption of IoT-based healthcare devices among end users and suggest a predictive model of adoption. The model is based on the UTAUT2 with newer variables identified from the literature. The sample ( n = 253) was collected from four major cities in India, and partial least squares-structural equational model was used to assess the measurement and the structural model. The factors such as ubiquitous, social influence, perceived health risk, and relative advantage had a significant influence on attitude (ATT), which influences behavioral intention (BI) toward IoT-based healthcare devices. Facilitating condition (FC) and price value did not show any significant influence on ATT toward the technology, but FC had a direct influence on BI. The study helps in advancing IS research by adding new variables to the existing knowledge and proposing a model based on UTAUT2. Furthermore, it also brings important practical implications for practitioners.

In recent years, there has been a noticeable rise in the mortality rate, and heart disease is a significant contributor to this trend. According to the California Association for Diabetes Investigation, by 2015, cardiovascular disease would be the primary cause of death in India, where 62 billion people live. Deficiencies in the heart’s ability to pump blood to and from the rest of the body are the leading cause of cardiovascular disease. The healthcare industry is a prime example of a sector poised to greatly benefit from the availability of massive amounts of data and analytical insights. Increasingly, it will be important to extract medical data to predict and treat the high fatality rate caused by heart attacks. Every day, humanity generates terabytes worth of data. Medical errors with dire effects can be avoided only with high-quality services. Hospitals can reduce the price of expensive clinical testing by using decision support systems. Hospitals in the modern-day use hospital information systems to keep track of patient records. The health care sector generates vast amounts of data, but little of it is really put to good use. It will be important to adopt a new strategy to reduce costs and make accurate predictions about heart disease. To determine which machine learning and deep learning approaches are most useful and accurate for predicting and classifying cardiac illnesses, this article reviews the existing literature on the topic and subsequently tries to detect the most probable factors leading to heart disease. This study introduces and models an artificial neural network methodology for identifying potential cardiovascular disease risk factors. In this study, we examine and present the various full and partial correlations among risk attributes. In addition, a number of risk variables are analysed to generate a predicted list of risk features most likely to result in cardiovascular disease.

The mechanization of farming is currently the most pressing problem facing humanity and a burgeoning academic field. Over the last decade, there has been an explosion of Internet of Things (IoT) application growth in agriculture. Agricultural robotics is bringing about a new era of farming because they are growing more intelligent, recognizing causes of variation on the farm, consuming fewer resources, and optimizing their efficiency to more flexible jobs. The purpose of this article is to construct an IoT-Fog computing equipped robotic system for the categorization of weeds and soy plants during both the hazy season and the normal season. The used dataset in this article included four classes: soil, soybean, grass, and weeds. A two-dimensional Convolutional Neural Network (2D-CNN)-based deep learning (DL) approach was implemented for data image classification with dataset of height and width of 150 × 150 and of three channels. The overall proposed system is considered an IoT-connected robotic device that is capable of applying classification through the Internet-connected server. The reliability of the device is also enhanced as it is enabled with edge-based Fog computing. Hence, the proposed robotic system is capable of applying DL classification through IoT as well as Fog computing architecture. The analysis of the proposed system was conducted in steps including training and testing of CNN for classification, validation of normal images, validation of hazy images, application of dehazing technique, and at the end validation of dehazed images. The training and validation parameters ensure 97% accuracy in classifying weeds and crops in a hazy environment. Finally, it concludes that applying the dehazing technique before identifying soy crops in adverse weather will help achieve a higher classification score.

The track state detection is of great significance to timely understand the operation state of track and find track defects and prevent operation accidents. This article initially analyzes the key technologies of track detection system and then proposes an image detection technology and image processing method for analyzing track detection at home and abroad, thus putting forward the scheme of track detection using image processing. The characteristics of onsite track images are analyzed, and a track state detection system based on track image preprocessing, image position correction, image defect comparison, and track section size measurement is designed in this article. Further in this article, a study of image linear transformation, noise filtering, defect recognition, and edge detection in track image processing is applied. Furthermore, a robust piecewise linear transformation is designed using the combination of image threshold transformation and image gray transformation. It reduces the loss of detailed information in the process of image processing. The center point of track bright band is determined by the image region segmentation method, which effectively reduces the error of image track measurement and improves the measurement accuracy.

To study the control optimization of conical interference connection system for large piston compressor, the reliability optimization and reliability optimization design of conical interference connection were systematically described. By using the proposed method, the maximum operating reliability can be obtained under the condition of constant interference joint size, namely, reliability optimization. Alternatively, the desired working reliability can be designed into the mating connection by optimizing parameters (combining size and interference), namely, reliability optimization design. Design value of 2.12 compression ratio is significantly higher than the unit level. As a result, the mid-stage exhaust temperature of the unit is as high as 124°C, and it is in high temperature operation state. It has been proved that conical interference connection is widely used in hydraulic transmission device because of its advantages of simple structure, reliable operation, and ability to bear certain impact load, reliable transmission torque than general connection, and convenient assembly and disassembly and has been proved to be reliable in practice.

A research challenge of vibration and control optimization of pressurized reducer is solved in this article; a method based on genetic algorithm (GA) is proposed to optimize the vibration and control of reducer. Considering the bending strength of helical gear root and tooth surface contact fatigue strength as constraints, the improved GA is used to solve it, and the optimal parameter combination is obtained. The size of center distance is reduced by 9.59% compared with that before. Based on the optimized results, the vibration becomes weaker with the increase of the load at the output end of the reducer, and its maximum value is only 1/8 of that when the load is 550 N. The experimental results show the optimized surface load distribution of driving gear teeth. The maximum normal load per unit length of the optimized output stage driving gear surface is 521.321 N/mm, which is significantly lower than the 662.455 N/mm before optimization. At the same time, the tooth surface load is evenly distributed. The larger tooth surface load is mainly distributed in the middle of the tooth surface with strong bearing capacity, which effectively solves the problem of unbalanced load before optimization and improves the bearing capacity of gear transmission. It is proved that GA can effectively realize the vibration and control optimization of pressurized reducer.

In order to solve the surface defects such as white silk, spots and wrinkles in the process of digital printing, a surface defect detection system for printed fabrics based on accelerated robust feature algorithm was proposed. Image registration is mainly carried out through accelerated robust feature (SURF); bidirectional unique matching method is adopted to reduce mismatch points, achieve accurate image registration, and extract defect information through differential algorithm. The performance of the improved surfing algorithm is verified by using multiple images. The experimental results show that compared with the traditional template matching method, the detection accuracy of the system detection algorithm is 12% higher, and the average time is 42.81 ms shorter than the traditional template matching method. Experiments show that the improved surfing algorithm has short time and high precision. The system can meet the actual production needs. The new system can detect surface defects on printed fabrics with an accuracy of 98%. Conclusion: The algorithm has higher detection rate and faster detection speed, which can meet the needs of practical industrial applications.

Improved Reliability and Low Latency Communication (IRLC) with Augmented Reality (AR) has become an emerging technology in today’s world. To minimize an accessory adaptation for Customer Equipment (CE) in AR, it may be feasible to offload the AR workload onto the onboard devices. Mobile-Edge Computation (MEC) will improve the throughput of a CE. MEC has caused enormous overhead or communication omissions on wireless media, making it difficult to choose the optimal payload proposition. The proposed system explores on-board devices that work together to achieve an AR goal. Code splitting is a Bayesian network used to examine the overall interdependence of efforts. From a longevity and endurance perspective, it is used to reduce the Probability of Supplier Failure (PSF) of an MEC-enabled AR environment. Weighed Particle Swarm Optimization (WPSO) was proposed despite the reality based on the emphasis on balancing the issue. As a result, a heuristic-based WPSO facilitates to improve the performance measures. A hybrid method could significantly increase the assertion of a predicted PSF in various network scenarios compared to the existing communication technologies. A preliminary iterative approach is suitable for AR operations and IRLC scenarios to generalize the attributes.

To better extract the characteristics of rolling bearing vibration signals, the author proposes a method based on improved genetic algorithm-particle swarm optimization (GA-PSO) algorithm. The common time-domain and frequency-domain feature index construction vectors were extracted based on vibration signals, for signal prediction, by establishing an improved particle swarm algorithm, and by optimizing the signal feature model of the support vector machine (SVM), the signal of the rolling bearing was predicted. The experimental results show that: After the author’s improved particle swarm algorithm optimizes SVM, the signal characteristic accuracy of the bearing is significantly higher, the regression fitting curve is smoother, although the fitting trend is basically the same, the error is significantly higher, this shows that it is feasible to optimize SVM’s rolling bearing signal characteristics based on particle swarm optimization, and proved the author’s improvement of the particle swarm algorithm, it is effective in optimizing SVM parameters. It is proved that the improved GA-PSO algorithm can better extract the characteristics of the vibration signal of the rolling bearing.

The article introduces a novel strategy for efficiently mitigating COVID-19 distribution at the local level due to contact with any surfaces. Our project aims to be a critical safety shield for the general people in the fight against the epidemic. An ultrasonic sensor is integrated with the automated doorbell system to ring the doorbell with a hand motion. A temperature sensor Mlx90614 is also included in the system, which records the temperature of the person standing in front of the door. The device also includes a camera module that captures the image of the person standing at the front entrance. The captured image is processed through an ML model which runs at over 30 fps to detect whether or not the person is wearing a mask. The image and the temperature of the person standing outside are sent to the owner through the configured iOS application. If the person outside is wearing a mask, one can open the door through the app itself and permit the entry of the person standing outside thereby integrating the edge device with an app for a better user experience. The system helps in reducing physical contact, and the results obtained are at par with the already existing solutions and provide a few advantages over them.

To save network energy consumption and prolong network life cycle in complex mechanical fault diagnosis, a research method of data fusion routing protocol algorithm based on wireless sensor network (WSN) is proposed. The specific content of the method is as follows: First, the low-energy adaptive clustering hierarchy algorithm is analyzed and discussed. On this basis, the prim route fusion algorithm is proposed to realize the effective utilization of energy and prolong the life of the network. Then, the WSN is abstracted as an undirected graph. From the perspective of saving the energy of the whole network, several current algorithms for building fusion trees are compared. The experimental results show that the prim algorithm consumes energy only after 700 rounds of clustering, while the leach clustering algorithm consumes energy only after 500 rounds. This shows that applying the prim algorithm can reduce the energy consumption of the whole network and prolong the life cycle of the network. However, the algorithm is carried out on the premise of uniform distribution of nodes, and there is a certain gap with the specific application of WSN in mechanical fault diagnosis. In the comparison of node energy consumption, it is found that compared with using the shortest path tree, using the central point of graph algorithm can greatly save the energy consumption of the node and has better performance. Practice has proved that this method can effectively remove redundant data information and solve the problem of unreliable data collected by a single sensor node. It is more suitable for the specific application of WSN in mechanical fault diagnosis.

To improve the accuracy of the mechanical fault diagnosis of the operating mechanism and fully exploit the characteristic information in the vibration signal of the high-voltage circuit breaker, a mechanical fault diagnosis method of the operating mechanism of the high-voltage circuit breaker based on the deep self-encoding network is proposed. First, the vibration signal of the switch operating mechanism is extracted, the wavelet packet conversion is performed, and the vibration signal of each frequency band is divided into equal times. The energy of the time–frequency subplane of the vibration signal is then calculated, and the time–frequency energy distribution is used as a switch. Finally, a breaker failure diagnostic model based on the deep self-coding network is established. Pretraining and tuning and a 126 kV high-voltage switch are used to simulate different types of faults and validate the method. Experimental results show that this method can acquire sample failure data and perform failure diagnosis, and the diagnosis accuracy rate reaches 97.5%. The deep self-coding network can fully pierce deep information on the switch vibration signal.

Research proposes an optimal power distribution approach for application of electric vehicle (EV) with use of hybrid energy storage system (HESS). HESS protection structure includes dual isolated-based soft-switching symmetrical coupling with half-bridge bidirectional converters to the system of battery and supercapacitors (SCs). The bidirectional converter properly controls the charging process of the battery and SC as well. Besides, spiral wound SCs of mesoporous electrode material have been used in EVs. In the drive cycle of EVs, the operation of SC relates to the functionality of the allocated scheme under “peak load transfer” at 2 i sc ∼ 3 i bat current profile carried out. New energy allocation strategies under SOC control enable SC charging and discharging at peak currents of around 4 i bat . The comparison of the mode of the battery system showed that the performance acceleration built under EVs has been improvised at a certain rate of 50% with a loss of energy minimised to 69%. As a result, the technique adapts different load curves, thus enhancing the utilisation of energy with reduced aging of the battery. The simulation results show that the proposed scheme meets the power demand of a typical driving cycle, for testing vehicle performance, and various energy management system have been assessed based on hydrogen consumption, overall efficiency, state of charge of SCs and batteries, stress on hybrid sources, and DC bus stability. The proposed strategy reduces hydrogen consumption by 8.7% compared to other strategies.

With proper tuning in the phase shift of reconfigurable intelligent surfaces (RISs) or intelligent reflecting surfaces (IRSs), which consist of huge number of passive reflecting elements, are the most promising and transforming technology to reconfigure the wireless propagation ecosystems to improve the “spectral-and energy-efficiency” for the future generation wireless communication system. This article presents a systematic review of RIS/IRS technology and conceptualizes the prospective requirement, operations, and application. Further, we have presented its capability for execution in a forthcoming smart industrial ecosystem to assist the development of Industry 5.0 with a series of emerging applications. Finally, we discuss the potential, open research challenges, and prospects of RIS technology in the emerging intelligent manufacturing industry to encourage future research in this area.

A fuzzy-integrated sliding mode-based hybrid controller (FISMHC) attributed to unified power quality conditioner (UPQC) was proposed in this study through implementation with solar integrated to fuel cell through incorporation of UPQC within sequence designed for active power filters of series and shunt configurations under shared structure of DC-link capacitor deployment. Furthermore, the proposed scheme with FISMHC UPQC (U-FISMHC) can achieve the following goals: (i) maintaining constant DC-link voltage in the absence of peak overshoot, (ii) performance evaluation under varied fluctuations in grid voltage, and (iii) decreasing source current and load voltage harmonics. In addition, the study compares U-FISMHC performance with distribution case over specific test conditions such as supply voltages, solar irradiation, and conditioned loads to demonstrate the proposed controller’s superior performance.

The demand for electricity globally has led to the search for renewable energy resources for power generation and attaining it in an eco-friendly manner. The solar photovoltaic systems and wind-based generators of power are regarded as primary resources of renewable energy and are called Distributed Generation units as they are scattered in nature. These are operated with bidirectional converters by providing auxiliary services at grid side and load side in either mode of microgrid operation. Besides, the DC power generation units’ integration gets converted into AC system by means of inverters. These types of systems not only increase voltage and current harmonics and power frequency deviations but also drive the distribution system to risky operating zone. This emphasizes the stipulation of advanced control schemes for microgrid architecture. Consequently, power electronic converters introduce harmonics in the system and affect system performance. To report these expanded issues, the authors recognized an advanced custom power device entitled Distributed Power Flow Controller (DPFC). In this study, the proposed system on solar–wind-based hybrid energy approach has been examined primarily through the strategy of DPFC mechanism. Later, the system has been examined with Genetic Algorithm (GA)-based fuzzy logic controller and GA-based adaptive neuro fuzzy inference system controller for shunt control of context built with DPFC mechanism. Furthermore, the validated results are verified using MATLAB/Simulink software.

An optimization approach is described in the research study that deals with the issue of reconfiguration networks built with certain conditions of power loss reduction and reliability. Furthermore, the reconfigured networking system seeks optimization based on criteria affecting the limitations. This study optimises specific network faults subjecting resources with no supply during reconfiguration to avoid the effect and possess through active power losses. These goals were met using the mathematical method of the optimisation process. The mathematical formulation is generated first in the system development process. As a result, a comprehensive methodology using genetic algorithm, Grey Wolf optimization (GWO), and particle swarm optimization (PSO) was developed. Finally, intended methodologies were estimated. Based on the results, it is clear that the proposed hybrid GWO-PSO approach outperforms all other methods in terms of node voltage, reliability, line currents, and computational duration. Furthermore, when optimally sized distributed generations are placed in optimal locations, total loss is reduced by up to 63% and voltage profiles improve.

Healthcare sector has become one of the challenging sectors to handle patient records as well as to provide better treatment to patients within a limited period. Covid-19 also exposed the limitations of the healthcare system due to the lack of better services. So, the involvement of information and communication technologies (ICTs) with the healthcare sector brings radical changes at global as well as local levels such as in hospitals and dispensaries. The article enlightened a novel survey technological paradigm that helps to facilitate the digital healthcare. With the use of technologies, the healthcare sectors are becoming more digital, innovative, patient-centric, and more effective. This article explores the proposed technological developments such as real-time health monitoring, generation of electronic health records, patient health record, mhealth, robotics, as well as robot sensors that are associated with healthcare sectors. This article also highlights the role of ICTs in different healthcare-related fields such as education, hospital management, health-related research, and data management as well as lightening the delivery levels of healthcare services. The article deals with the robotic applications in the healthcare field. This article categorizes the technologies as current and futuristic technological innovations enabling healthcare-as-a-service with benefits.

Artificial intelligence (AI), robotics, cybersecurity, the Industrial Internet of Things, and blockchain are some of the technologies and solutions that are combined to produce “smart manufacturing,” which is used to optimize manufacturing processes by creating and/or accepting data. In manufacturing, spark erosion technique such as wire electric discharge machining (WEDM) is a process that machines different hard-to-cut alloys. It is regarded as the solution for cutting intricate parts and materials that are resistant to conventional machining techniques or are required by design. In the present study, holes of different radii, i.e. 1, 3, and 5 mm, have been cut on Nickelvac-HX. Tapering in WEDM is a delicate process to avoid disadvantages such as wire break, wire bend, wire friction, guide wear, and insufficient flushing. Taper angles viz. 0°, 15°, and 30° were obtained from a unique fixture to get holes at different angles. The study also shows the influence of taper angles on the part geometry and area of the holes. Next, the artificial neural network (ANN) technique is implemented for the parametric result prediction. The findings were in good agreement with the experimental data, supporting the viability of the ANN approach for the evaluation of the manufacturing process. The findings in this research provide as a reference to the potential of AI-based assessment in smart manufacturing processes and as a design tool in many manufacturing-related fields.

Internet of Things (IoT) is a physical network of physical devices, such as widgets, structures, and other objects, which can store program, sensors, actuators, and screen configurations to allow the objects to assemble, control, display, and exchange data. The aim of this research was to develop an autonomous system with automated navigation. Using this approach, we are able to make use of deep neural networks for automatic navigation as well as the identification of pot holes and road conditions. Additionally, it displays potholes in traffic and the correct lane on the screen. The system stresses how important it is to select the path from one node to the next.

This article discusses the topic of ethics and policy for human interaction with robots. The term “robot ethics” (or roboethics) is generally concerned with ethical problems that may occur when humans and robots interact in social situations or when robots make decisions which could impact human well-being. For example, whether robots pose a threat to humans in warfare, the use of robots as caregivers, or the use of robots which make decisions which could impact historically disadvantaged populations. In each case, the focus of the discussion is predominantly on how to design robots that act ethically toward humans (some refer to this issue as “machine ethics”). Alternatively, robot ethics could refer to the ethics associated with human behavior toward robots especially as robots become active members of society. It is this latter and relatively unexplored view of robot ethics that this article focuses on, and specifically whether robots will be the subject of discriminatory and biased responses from humans based on the robot’s perceived race, gender, or ethnicity. If so, the paper considers what issues are implicated, and how society might respond? From past research, preliminary evidence suggests that acts of discrimination which may be directed against people may also be expressed toward robots experienced in social contexts; therefore, discrimination against robots as a function of their physical design and behavior is an important and timely topic of discussion for robot ethics, human–robot interaction, and the design of social robots.

A method based on the contour coefficient and image processing technology is proposed to better identify the visual elements. This article takes the contour of the image as the recognition feature, summarizes the methods of target contour feature extraction, contour shape representation, and similarity representation, and studies the processing methods of contour edge preserving and denoising, contour feature simplification and description methods, and contour matching methods. This problem can usually be solved by filling out a form. Generally, a simple iterative equation is given to express the direct relationship between the current table and the calculated table values. The dynamic programming algorithm of inner distance shape context, multi-scale convexity convexity, and triangle area representation finds the best sequence correspondence.

Aiming at the data preprocessing requirements and label data cost issues arising from the intelligent operation and maintenance of electromechanical equipment, this article mainly studies structured data cleaning methods and fault prediction algorithms for a small number of label samples. First, this article introduces the overall architecture of the intelligent operation and maintenance system for electromechanical equipment. Second, based on the electromechanical equipment operation and maintenance data access service, data cleaning, and fault prediction, this article constructs an electromechanical equipment intelligent operation and maintenance platform based on Kafka message queue, Spark cluster, and other components, and introduces the functional composition of the system in detail. Finally, the article describes the functions of each component of data access service, data cleaning, and fault prediction in detail. To address the cost issue associated with sufficient labeled sample data for data analysis, we propose a semi-supervised learning algorithm, IF-GBDT, based on improved independent forests and Gradient Boosting Decision Tree. The independent forest algorithm supplements labels for unlabeled data based on the learning results of a small number of labeled samples. We also use the gradient lifting tree algorithm to train the model based on the new tag data set for fault prediction, thereby reducing the impact of lack of tags on the accuracy of the prediction model. Experiments show that this method improves classification accuracy and has good adaptability and concurrency performance for a small number of tags.

To solve the problems of low efficiency and high risk of crack detection in high-rise buildings, the author proposes a building crack image acquisition and processing system based on unmanned aerial vehicle (UAV), expounds the working principle of the system, and analyzes the selection of hardware in the system. The experiment is carried out by combining the acquisition system with the image processing system of the ground station. Based on the characteristics of building surface crack image, an adaptive filtering method is introduced to improve the classic system file checker combination method. The experimental results show that the accuracy of Canny’s method was 96.88%. Canny-based crack target detection method is trained and tested on the crack data set to obtain the classification results and the best detection effect. It is proved that the Canny algorithm based on UAV images can effectively detect cracks on building surface.

The intelligent production line is generally composed of industrial robot workstations or multiple workstations, with complex process, large equipment investment, and difficult on-site debugging. In order to solve the problem of difficult on-site debugging, virtual simulation debugging technology has been gradually applied to the debugging of industrial robot workstations and intelligent production lines. In order to improve the debugging efficiency and safety of the industrial robot workstation, taking the industrial robot simulation assembly workstation as an example, the authors propose a virtual simulation debugging method based on the simulation software OCTOPUZ for the industrial robot workstation and Siemens programmable logic controller (PLC). We created an OCTOPUZ simulation model for an industrial robot workstation using SolidWorks and completed offline programming and PLC programming of the industrial robot in a personal computer based on the workstation’s task flow. In response to the problem of information exchange between simulation software OCTOPUZ and PLC during the simulation of industrial robot workstations, OLE for process control technology is adopted, with PLC as the server and OCTOPUZ as the client, in order to achieve real-time interaction of control signals between PLC and simulation software OCTOPUZ; thus, the virtual simulation joint debugging of PLC and industrial robot workstation was completed. The effectiveness of the virtual debugging scheme of industrial robot workstation and the correctness of industrial robot program and PLC program are verified. In practice, the simulation debugging before the equipment construction can find and solve the program problems in advance, and improve the efficiency of the workstation design programming and debugging. Through simulation, problems in the design of industrial robot control programs, PLC control programs, and other electromechanical components during workstation assembly can be identified and solved in advance, saving the cost of actual equipment debugging, thus improving the debugging efficiency and safety of actual equipment.