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The design engineering is one of essential work in modern manufacturing environment. The optimization is principal technique to be used widely for searching the solution. However, primary process of optimization is to know the relation between design input parameters and target output. In this work, an artificial neural network (ANN) approach as an intelligent algorithm is proposed to construct the relation and also provides it in form of mathematic modeling. Even though the ANN modeling is so call a backblock due to difficulty to understand complicated equations, it is simply constructed by automate iteration process. A case of paper helicopter is used as an example of the application. The classical 2k Factorial design is used to provide an experiment plan to create training and testing data. 93 experiments are carried out. The architecture of ANN is set according to lowest Mean square error (MSE) of training and testing procedure. The result of 5-10-1 architecture has shown ability to accurately predict output, landing time, with MSE of 0.012. With such a highly quantitative accuracy of results, the developed model using the neural network approach can be used for finding the suitable input parameters to achieve a desired target output. In this case, the design of dimension (A) Depth of cut wing is 1.3 cm., (B) Length of wing is 12.9 cm., (C) Length of body is 9.0, (D) Width of body is 2.0 cm., and (E) Depth of cut body is 0 cm. yield the lowest area of a paper helicopter that can meet the target landing time, 2.85 + 5% second.

This article introduces the use of PCI software production day draw satellite is projective like production process, analyzes the key technologies in the production, summarizes the Suggestions to improve the quality of results, strengthen quality control method is proposed, for the day draw satellite orthogonal projection as mass production work has carried on the beneficial exploration.

The development of technologies for the maintenance industry has taken an important role to meet the demanding challenges. One of the important challenges is to predict the defects, if any, in machines as early as possible to manage the machines downtime. The vibration-based condition monitoring (VCM) is well-known for this purpose but requires the human experience and expertise. The machine learning models using the intelligent systems and pattern recognition seem to be the future avenue for machine fault detection without the human expertise. Several such studies are published in the literature. This paper is also on the machine learning model for the different machine faults classification and detection. Here the time domain and frequency domain features derived from the measured machine vibration data are used separated in the development of the machine learning models using the artificial neutral network method. The effectiveness of both the time and frequency domain features based models are compared when they are applied to an experimental rig. The paper presents the proposed machine learning models and their performance in terms of the observations and results.

Companies are under increasing pressure from customers, competitors, limited resources, etc. These conditions oblige companies to permanently improve their organizational structure and the activities of all system’s flows. The paper analyses the types of Kanban systems, in the broader context of the enterprise systems. The most important reference architectures were analysed (with the emphasis on the modules dedicated to flow description) and the standards that allow the activity modelling of a manufacturing enterprises. Starting from this context, the paper describes three models of Kanban systems that are specific to companies with different information systems development. Models are: Push production with withdrawal Kanban; Pull production with withdrawal Kanban and production Kanban triggered by Kanban board; Pull production with withdrawal Kanban and production Kanban triggered by the dispatch to the customers. The models are graphically described using Value Stream Map (VMS) symbols. These models are useful in the development and improvement of production flows.

The utilization of regenerative braking energy is of great significance to the energy saving of subway. Therefore, this paper proposes an optimization method for scheduling and control integration, which not only adjusts the timetable but also optimizes the speed curves of trains. When there is a train braking, this method will try to find a train that accelerates to absorb the regenerative energy generated by the braking train. Firstly, this paper establishes the timetable energy saving optimization model, based on which the speed curves will be optimized. Furthermore, we design a scheduling optimization algorithm based on genetic algorithm, and optimizes speed curves of trains by binary search method to obtain a good solution. Finally, simulations are given using the real data of Beijing Metro Line 4 to evaluate the proposed method, and the results show that the integrated scheduling and control optimization method can reduce energy consumption by 15.18%. In the random disturbance simulations, the proposed method shows good robustness, which makes it possible to apply this method to the real subway operations.

This paper proposes a hybrid financial time series forecast model based on LSTM and LightGBM, namely LSTM_LightGBM model. Use the LightGBM model to train the processed stock historical data set, and save the training results. Then the opening price, closing price, highest price, lowest price, trading volume and adjusted closing price are separately input into the LSTM model for prediction. The prediction result of each attribute is used as the test set of the prediction after LightGBM training. Constantly adjust the parameters of each model, and finally get the optimal stock price forecast model. The model is validated with the rise and fall of AAPL stock. Through the comparison of evaluation index root mean square error RMSE, mean absolute error MAE, prediction accuracy Accuracy and f1_score. It is found that the LSTM_LightGBM model exhibits stable and better prediction performance in the stock prediction. That is to say, the LSTM_LightGBM model proposed in this paper is stable and feasible in the stock price fluctuation forecast.

The construction of index system is the basis of equipment system contribution rate evaluation, and is also one of the main contents of equipment test and appraisal. This paper discusses the concept of systems contribution rate, introduces the evaluation process of system contribution rate of information system equipment, and gives the construction principles and ideas of information system equipment system contribution rate evaluation index system. Then, referring to the theory of operational loop, the evaluation index system of system contribution rate is given from the perspectives of system capability and system effectiveness. The evaluation index of this paper can provide decision support for the development of equipment planning system.

Convolution neural network for remote sensing image scene classification consumes a lot of time and storage space to train, test and save the model. In this paper, firstly, elastic variables are defined for convolution layer filter, and combined with filter elasticity and batch normalization scaling factor, a compound pruning method of convolution neural network is proposed. Only the superparameter of pruning rate needs to be adjusted during training. in the process of training, the performance of the model can be improved by means of transfer learning. In this paper, algorithm tests are carried out on NWPU-RESISC45 remote sensing image data to verify the effectiveness of the proposed method. According to the experimental results, the proposed method can not only effectively reduce the number of model parameters and computation, but also ensure the accuracy of the algorithm in remote sensing image classification.

With the development of new generation Internet technology, Digital Object Architecture (DOA)/Handle system plays an important role in industrial system. The development and technical characteristics of DOA/Handle technology is studied in this paper. Then function architecture of distributed data acquisition system based on DOA/Handle technology is proposed. Through the detailed design of the functions and characteristics of the architecture, the core functions such as distributed data acquisition, distributed data management, data security sharing, and data security governance are realized in the industrial Internet environment. At the end of the paper, the typical scenarios of this architecture are summarized, and a prospect of the application prospect of DOA/Handle technology is introduced.

This paper introduces the structure of the existing on-line monitoring system, and analyzes the problems of poor compatibility and reliability of IED in different manufacturers. Then it gives two ways to transform the on-line monitoring system of substation equipment to the Internet of things, which is to retain IED and to remove IED. On this basis, this paper introduces the application scenarios of the on-line monitoring system of substation equipment connected to the Internet of things, including accurate perception of equipment status, accurate evaluation of equipment status and intelligent research and judgment of equipment status.

In this paper, I-GWO-KELM algorithm is used for short-term power load forecasting. Normalize the power data and meteorological data of the short-term power load, and use GWO to optimize the regularization coefficient of KELM and the RBF kernel parameters. To apply the model to short-term power load forecasting to obtain simulations for the next 24 hours and 168 hours curve. Experiments show that the improved model I3-GWO-KELM proposed in this paper has the best effect. The improvement of GWO in this paper is effective and feasible. In the application of short-term power load forecasting, the IGWO-KELM model is more accurate than the ELM and KELM models.

The current rapid development of cloud computing and networks has put forward new requirements for the construction of new infrastructure such as data centers. This paper compares traditional data centers and high-density data centers, proposes a three-tier infrastructure for high-density data centers, and analyzes the data center environment. To solve the obvious problem of software and hardware heterogeneity in high-density data centers, this paper uses virtualization technology to pool resources in high-density data centers and introduces SOA architecture to manage software and hardware resources hierarchically. Finally, the data center infrastructure and environment matching technology are studied.

UAV needs sensor to fly in an environment with obstacles. However, UAV may not be able to move forward when it encounters a large obstacle, or UAV will be in a dangerous state when the sensor fails briefly which disturbed by the environment factors. In order to solve these problems, the following methods are proposed in this paper. Aiming at the first problem, this paper proposes an improved APF method for path planning, and verified by simulation experiments that this method can find the optimal path. Aiming at the second problem, this paper proposes a solution to expand the range of obstacles and dynamically change the distance in the APF repulsion function. It is verified that the UAV can fly safely within the short time of the sensor problem by simulation experiments. In conclusion, this paper has an important reference value for the application of UAV online dynamic path planning in engineering.

The interaction history between users and items is usually stored and displayed in the form of bipartite graphs. Neural network recommendation based on the user-item bipartite graph has a significant effect on alleviating the long-standing data sparseness and cold start of the recommendation system. The whole paper is based on the bipartite graph. An review of the recommendation system of graphs summarizes the three characteristics of graph neural network processing bipartite graph data in the recommendation field: interchangeability, Multi-hop transportability, and strong interpretability. The biggest contribution of the full paper is that it summarizes the general framework of graph neural network processing bipartite graph recommendation from the models with the best recommendation effect in the past three years: embedding layer, propagation update layer, and prediction layer. Although there are subtle differences between different models, they are all this framework can be applied, and different models can be regarded as variants of this general model, that is, other models are fine-tuned on the basis of this framework. At the end of the paper, the latest research progress is introduced, and the main challenges and research priorities that will be faced in the future are pointed out.

The paper describes the methodology of annotating of road accidents centres of interactive accidents map. Based on the statistical analysis of the traffic accidents database the range of accident risk zones of a roadmap of the city are allocated as well as cause of accidents. These information in the form of text messages is applied to a vector map of a geographical area, in this paper – the map of Nizhny Novgorod. The annotation algorithm also takes into account the data of the road infrastructure, the weather, the driver (the user of the interactive crash map). The method was tested during the creation of e-cards of Nizhny Novgorod.

In this paper, a new variant of the Heterogeneous Dial-a-Ride Problem (HDARP) is innovatively applied to a routing problem of trucks picking up and delivering full and empty containers in the logistic chain, including container depots, importers demanding full containers and exporters requesting empty containers. On the route different types of trucks pick-up and deliver full and empty 20- and 40-foot containers. We consider trucks that are operated by alternative fuel, as well as the need for refuelling of trucks in any alternative fuel station to eliminate the risk of running out of fuel during its route. The objective of the paper is to contribute to the optimization of container truck transport routing with multiple pick¬ups and deliveries by providing a mathematical programming model aiming at minimizing the transportation cost as well as the negative environmental impacts.

The construction industry often falls short of other industries in terms of innovation; overall productivity within the construction industry has remained nearly flat for fifty years. As such, the industry is frequently exploring new technologies with the potential to increase its work efficiency as well as productivity. Mixed Reality (MR), which allows for the blending of real and virtual worlds, is one such technology that has a capability to improve productivity. The aim of this research study is to test MR’s potential as an onsite assembly tool in construction. The study more specifically determined if holographic BIM models, spatially anchored in 1:1 ratio over active construction sites, could provide trades’ sufficient information to assemble and install their work without using paper plans. A controlled experiment was designed to test if the electrical and plumbing components of a mock-bathroom could be installed only with MR. A total of 5 sub-groups, each consisting of two participants, took part in this experiment and the time is taken by each sub-group (i.e. the MR group) to complete the assembly was recorded. This was then compared with the time taken by a similar number of sub-groups (i.e. the Control group) to complete the same assembly using paper plans only. The analysis indicates that on average the MR group took approximately 9% less time as compared to the Control group. Qualitative analysis via a post-exercise questionnaire found several technological limitations that the MR must overcome before it sees widespread implementation as a tool for guiding construction assembly. The post-exercise questionnaire also gathered participants’ perceptions about MR potential in construction. This paper highlights the benefits and limitations of the MR technology in construction along with recommendations for improvement as well as future research.

The paper identifies the possible energy sources that an electric vehicle can use during its operation to increase energy autonomy. The identified sources are modeled and simulated, and the results are interpreted in terms of viability. The paper presents the constructive principles of a power regeneration system consumed by an electric vehicle during operation to increase its autonomy. It also identifies the possible energy sources that an electric vehicle can use during its operation. The concepts of electricity regeneration are modeled and simulated, and the results are interpreted in terms of viability.

The concept of minimizing human error in maintenance is progressively gaining attention in various industries. The incorporation of human factors when solving engineering problems, particularly in maintenance, can no longer be ignored where high standards of performance are expected. The journey of improving maintenance performance through the reduction of human error begins with the understanding of causes and impact of human error in maintenance. This paper evaluates previous scholarly writings on human errors, to specifically establish the causes and impact of human error in maintenance. This study relies predominantly on the existing literature on human error in maintenance derived from published and unpublished research. The primary findings emerging from the research exhibit a number of key factors that cause a human error in maintenance such as poor management and supervision, organizational culture, incompetence, poorly written procedures, poor communication, time pressure, plant and environmental conditions, poor work design and many more. The literature review also revealed that human errors have a negative impact on safety, reliability, productivity and efficiency of the equipment. It was further discovered that equipment failures leading to accidents, incidents, loss of life and economic losses are the major effects of human error. Human error in mechanical systems’ maintenance is a serious problem which needs adequate attention in order to develop corrective and preventive measures. This review paper serves as a basis for maintenance practitioners and interested parties to develop corrective and preventive measures for minimizing human error in the maintenance of mechanical systems.

Small unmanned aerial vehicles (SUAVs) have found a widespread application in past decades. However, as the criticality of the missions for which they can be used increases, the demand for improvement of their efficiency increases as well. The paper focuses on a propeller driven SUAVs of a multirotor type, equipped with an electric motor, battery and propeller. The paper presents a simplified method of calculation of the SUAV maximal endurance, if the characteristics of all components of the propulsion system are known. To improve the overall efficiency of the propulsion system of an SUAV, the correct combination of all propulsion system components is critical. However, the largest impact on the maximal endurance is, arguably, caused by the propeller. The paper proposes a simple method of optimizing the propeller characteristics for hover and compares the proposed propeller design with conventional and commercially available propellers.

In this paper, the authors present some of the theoretical and experimental research conducted on the occasion of rotary tables remanufacturing for CNC machine tools belonging to Boring and Milling Machines family. They give the constructive solutions applied on CNC machines and the specific systems of guiding, locking and driving. The authors present also the modifications required when passing from CC variant to CNC variant in terms of locking - indexing systems and hydraulic suspension systems. The paper presents different achievements but insists particularly on the remanufacture of the rotary table with useful area 1700x2000 mm with longitudinal travel of 1000 mm (MRD 1700x2000/1000).

This paper proposes a novel approach to dynamic safety level calculation for safety-critical systems based on the STAMP accident model and the implementation of a mathematical model. The proposed approach utilises (1) an STPA hazard analysis applied to the system in question, (2) system operational data from domain experts regarding process duration and reaction times, and (3) real-time system data. The STPA analysis is transformed into acyclic diagrams that graphically indicate every possible sequence of safety constraint violations that could lead to a loss (path). Based on this diagram the safety level (SL) of a system is defined as $ {\rm{SL}} = {\overrightarrow {\rm{p}} _w} $ where $ {\overrightarrow {\rm{p}} _w} $ is the most detrimental to safety path which is active for any possible time value or context in the system’s operation. This approach is also demonstrated using as a case study the “classical” Train Door STPA analysis example. This paper aims to introduce a new perspective on the problem of measuring and managing the actual safety level of highly complex socio-technical systems in real time and discusses related limitations and future research opportunities of this approach.

Every year there are about 3500-5200 people suffering from upper limb amputations, most of which are wrist disarticulation and transcarpal. This paper investigates current upper limb prostheses and presents the disadvantages of current prostheses, including limited degrees of freedom (DOF), limited range of motion, weight, customizability, and appearance. The proposed design is the first stage of a series of papers that proposes designs that are compatible with shape morphing materials. The use of these materials as actuators allows the development and design of more advanced upper limb prostheses. Therefore, the prosthesis is modelled as needed for patients with transcarpal/wrist disarticulation amputations. The proposed model has 27 degrees of freedom (DOF), reduced weight, low cost, improved appearance, and is printable to fit.

In view of the deficiencies of GPS in the urban environment, such as signal occlusion, multipath and Non-LOS, this paper proposes a solution that using a low-cost monocular camera instead of inertial navigation system to combine with GPS. Firstly, considering that the actual road conditions of the vehicle are mostly plane and in order to reduce the computational resources of visual odometer (VO), this paper establishes a VO solving model based on plane constraint; then the VO&GPS loose coupling model is proposed. The position error and yaw angle error of the two sensors are taken as input, and the errors are filtered by the adaptive Kalman filter. Experiments show that the positioning effect of VO&GPS loose coupling system is better than that of single GPS, and it can suppress the drift of VO, which can meet the positioning requirements when satellite signals are interfered in a short time.

With the development of “One Belt, One Road” initiative and free trade area, the volume of cross-border international logistics involving multiple modes of transport has surged. Meanwhile, the proportion of using integrated transportation system in domestic trunk transport has increased. Multi-modal transport (MMT) based on green transport can realize intensive utilization of transport capacity resources, and implement sustainable transport management with three bottom lines of economic, environmental and social aspects. In this paper, the carbon emission index and regional transportation infrastructure utilization index are introduced to construct a multi-objective optimization model with sustainable goals of environmental protection, cost saving and social contribution. The poly-population genetic algorithm (PPGA) is used to overcome the limitation of the traditional genetic algorithm running to the local optimum. The model proposed by this paper quantifies environmental and social indicators, balances comprehensive performance of environment, economy and society, and provides quantitative decision making support for carriers, international freight forwarder or third party logistics to carry out green MMT.

In this paper, based on the auto disturbance rejection control technology, the electromechanical servo system is used as the control actuator, and the thrust vector control of the solid rocket motor nozzle with typical nonlinear friction characteristics is studied and analyzed. In this paper, the realization of the classical PID algorithm and the lack of dynamic performance are analyzed, and then the compensation algorithm based on the auto disturbance rejection control technology is added. The algorithm compensates for the phase lag of the system due to the nonlinear friction characteristics. As a result, the frequency characteristics of the system have been significantly improved.

With the continuous innovation of optical remote sensing technology and the increasing demand for spatial information, satellite videos, which can provide higher spatial and temporal resolution, have been paid a lot of attention. And moving vehicles extraction in satellite videos is one of the most important tasks. By analyzing the shortcomings of current satellite video moving vehicles extraction algorithms, and combining with the characteristics of satellite videos and moving vehicles, this paper proposes an algorithm to extract moving vehicles in satellite videos, that some vehicles are firstly separated from the background by using image extreme points and mean differences, and then the moving vehicles are extracted by joint detection of inter-frame vehicles motion. At the same time, based on the extracted moving vehicles, we also propose a method that can extract road masks by using only three frames. Finally, we use Jilin-1 satellite video data to prove the proposed methods in the experiment. And also this paper has compared the propose methods with another two algorithms, where the results show that the proposed methods greatly improve the accuracy and quality of moving vehicles detection in satellite videos.

The paper deals the problem of the energy supply after disaster and emergency. The purpose of the paper is to review the autonomous power supply technology's application to provide electricity in the aftermath of disasters for emergency relief and to remote areas having no access to the grid. Safety requirements for electric power system facilities in case of a threat of violations and/or electricity supply fault are considered. The advantages of different types of autonomous electricity sources for disaster resilience are discussed. On the example of the Sakhalin Island, Russia, a conclusion about the difficulties of using solar electricity sources during the repair and recovery works after accidents and disasters at island territories is drawn. The paper concludes by emphasizing the importance of autonomous power supply technologies in engineering education and integrating them in programs related to alternative energy and disaster & emergency management.

This paper deals with the data processing in the form of a point cloud scanned using a handheld 3D laser scanner. The aim of the paper is to use this data to create a representative FEM model and compare two different approaches while one of them involves creating a CAD model and the other one not. The data processing is carried out in the freeware systems MeshLab and Meshmixer and FEM analysis in the software ANSYS Workbench. The part of the results of this work has been supported by VEGA grant No. 1/0795/16 and KEGA grant No. 017ŽU-4/2017.

In medical rehabilitation and physical therapy practice it has been proved that the passive mobilization of the hand joints as part of rehabilitation exercises improves the recovery period and also diminishes the rehabilitation costs by about 50%. In order to facilitate the practice of passive mobilization exercises in both medical units and patient homes the development of new adequate rehabilitation equipment has become a necessity. It is within the context of this requirement that the paper puts forward and discusses a novel solution of rehabilitation equipment that makes possible the simultaneous exercising of the radiocarpal, metacarpophalangeal and interphalangeal joints. The first novelty proposed by this equipment is using the Fin-Ray effect® in the construction of the hand support and mobilization structure. A further novelty is the utilization of a pneumatic muscle as the motor that drives the equipment. The paper presents the construction of the equipment and discusses range of motion generated by it compared to the corresponding motion limits of the human hand, as identified in literature.

The paper presents the model for the description of the dynamic system concerning the defect simulation. The system includes the initial part of the life cycle where same amount of the rework has to be considered because of the low quality work. The second subsystem considers a division of the maintenance as planned and unplanned activities. The model connects these subsystems in one complex model that allows us to perform the simulations for investigating different problems. The example of the calculation of the key system parameters is presented in the paper. The resultant values demonstrate the importance of the maintenance but also the impact of the initial phase of the life cycle that has substantial influence on the amount of the defects in the early period.

The paper presents an application of the modelling and simulation method for finding an optimum solution related to designing production processes. Using simulation in connection with optimization makes it possible to check almost all admissible variants of the proposed improvements comparatively quickly in order to evaluate them and to choose the best solution. The paper presents how a simulation model is created and shows an example of multi-criteria optimization.

This paper studies the diagnosis of twisted blade in a multi stages rotor system using adapted wavelet transform and casing vibration. The common detection method (FFT) is effective only if sever blade faults occurred while the minor faults usually remain undetected. Wavelet analysis as alternative technique is still unable to fulfill the fault detection and diagnosis accurately due to its inadequate time-frequency resolution. In this paper, wavelet is adapted and its time-frequency is improved. Experimental study was undertaken to simulate multi stages rotor system. Results showed that the adapted wavelet analysis is effective in twisted blade diagnosis compared to the conventional one.

Aiming at the problem of output current waveform distortion caused by superimposed high frequency components in the output current of varying polarity of VPPAW power supply. A novel adaptive PID controller with automatic switching is introduced in this paper, and the controller is established based on the evolution path of the operating point of the VPPAW power supply. Compared with the traditional linear PID controller, the adaptive controller proposed in this paper is very suitable for the control of arc load with strong dynamic characteristics and can effectively improve output waveform of the welding system.

In this paper, the construction of a set of comprehensive utilization of new energy system is put forward. The system can show the real time operation and simulated the actual system of the solar photovoltaic system, the solar-thermal systems and the air source heat pump water heater. And according to the running condition, energy saving and reduce carbon emissions for each new energy application situation can be calculated comparing to using traditional energy. Another reserved interface, which can be for the subsequent construction of other new energy systems (such as ground source heat pump system) is provided. This paper introduces the design scheme of the comprehensive utilization of new energy system.

The Bluetooth Low Energy (BLE) is an attractive solution for implementing low-cost, low power consumption, short-range wireless transmission technology and high flexibility wireless products, which working on standard coin-cell batteries for years. The original design of BLE is restricted to star topology networking, which limits network coverage and scalability. In contrast, other competing technologies like Wi-Fi and ZigBee overcome those constraints by supporting different topologies such as the tree and mesh network topologies. This paper presents a part of the researchers’ efforts in designing solutions to enable BLE mesh networks and implements a tree network topology which is not supported in the standard BLE specifications. In addition, it discusses the advantages and drawbacks of the existing BLE network solutions. During analyzing the existing solutions, we highlight currently open issues such as flooding-based and routing-based solutions to allow end-to-end data transmission in a BLE mesh network and connecting BLE devices to the internet to support the Internet of Things (IoT). The approach proposed in this paper combines the default BLE star topology with the flooding based mesh topology to create a new hybrid network topology. The proposed approach can extend the network coverage without using any routing protocol.

With the rapid development of technology, connected autonomous vehicle (CAV) is getting close to the reality. The application of CAV causes changes to road capacity, gas emission, public attitude and other realms. Lots of efforts have been spent in quantifying the potential changes and this paper is an attempt to review the relevant researches. There will be three sections, presenting review of the impacts on road capacity, environment and public attitude respectively. There is a large amount of papers making models to predict future road capacity with various penetration rate of CAV and they obtain quite different interesting results. To predict the future condition more properly, more stochastic models should be proposed. In terms of influence on environment, it may be hard to conclude whether CAV will exacerbate or relieve global warming by looking at current researches. It would be valuable to conduct a quantitative analysis on this issue. For the public attitude, this paper mainly focus on whether people are willing to use CAV and future efforts that may help with the promotion of CAV.

The graph optimization has become the mainstream technology to solve the problems of SLAM (simultaneous localization and mapping). The pose graph in the graph based SLAM is consisted with a series of nodes and edges that connect the adjacent or related poses. With the widespread use of mobile robots, the scale of pose graph has rapidly increased. Therefore, optimizing a large-scale pose graph is the bottleneck of application of graph based SLAM. In this paper, we propose an optimization method basing on the decomposition of pose graph, of which we have noticed the sparsity. With the extraction of the Single-chain and the Parallel-chain, the pose graph is decomposed into many small subgraphs. Compared with directly processing the original graph, the speed of calculation is accelerated by separately optimizing the subgraph, which is because the computational complexity is increasing exponentially with the increase of the graph’s scale. This method we proposed is very suitable for the current multi-threaded framework adopted in the mainstream SLAM, which separately calculate the subgraph decomposed by our method, rather than the original optimization requiring a large block of time in once may cause CPU obstruction. At the end of the paper, our algorithm is validated with the open source dataset of the mobile robot, of which the result illustrates our algorithm can reduce the one-time resource consumption and the time consumption of the calculation with the same map-constructing accuracy.

In the paper the main directions of the development of the methods for designing selected geometrical features of the mechanical components along with its representation in technical drawing were described. The influence of the shape deviations on the condition of the mechanical connections and its fits was defined and characterized. It was proved, that the shape obtained in the machining process significantly differs from the nominal shape of the designed parts. The consequence of the following deviations are the troubles during cooperation of the parts, which perform either rotary or reciprocating motion. The results presented in this paper contain the measurement of the real geometry of the journal and the hub along with determination of the value and the type of their shape deviations. Authors have proved that the magnitude of the shape deviations are subject to the errors connected with the methods of measurement and used tools. Proper simulations were performed, which shows the certain influence of the shape deviations on the cooperation of the mechanical parts considering both static and dynamic loading.

Incremental forming can be usually unfolded either on CNC milling machine–tools or serial industrial robots. The approach proposed in this paper tackles the problem of designing a modular fastening system, which can be adapted for both above mentioned technological equipment. The fastening system of the sheet–metal workpiece is composed of a fixing plate and a retaining plate. The fixing and retaining plates will be made up of different individual elements, which can be easily repositioned to obtain different sizes of the part. Moreover, the fastening system has to be able to be positioned either horizontally (to be fitted on CNC milling machines) or vertically (to be fitted on industrial robots. The paper also presents the design of a tool–holder working unit which will be fitted on KUKA KR 210 industrial robot. The working unit will be mounted as end–effector of the robot and will bear the punch, driving it on the processing toolpaths.

This paper presents the control algorithm for the new type of robot that balances on a single spherical wheel. This type of robot is called Ballbot and unlike other statically stable robots, it has a high gravity centre and a very small footprint. The robot is dynamically stable, which means that if the controller stops working, the entire construction will fall over. Because of that, it needs a special control algorithm to keep the balance. The presented Ballbot is fitted with sensors such as gyroscope and accelerometer and controls motors with omni-directional wheels to move the robot in any direction. This paper presents theoretical information about balancing robots and the most important elements of the robot. Next, the design concept of the controller based on STM32 family, control algorithms and filters were proposed and implemented. In the final section of this paper, the investigation results were presented and discussed.

The paper presents the analysis of heavy-loaded low-speed multi-pair spiroid gears under the effect of elastic contact, bending and shearing, and elastoplastic contact interaction of spiroid gearwheel teeth and spiroid worm threads. The main stages of the developed algorithm for analysis of load distribution and plastic strain of flanks with account of elastic and elastoplastic contact conditions, multi-pair contact and macro-roughnesses on flanks are described in short. At elastoplastic contact the zones of plastic contact on flanks of spiroid gearwheel teeth are determined and the value of plastic strain is analyzed. The flanks of the spiroid gearwheel teeth and worm threads are represented in the algorithm as the set of segments (cells) with coordinates of centers determined with account of the analyzed factors that influence the load distribution in spiroid gearing. These factors are the errors of manufacturing and (or) assembly, micro-roughnesses, and deformations of spiroid gear supports. The paper gives the numerical results of research of the real spiroid gears of gearboxes for pipeline valves (PV) at elastic and elastoplastic contact represented as summary tables and diagrams of contact stress distribution along contact areas and of plastic strain, that show the workability of the algorithm. The presented numerical results of the algorithm functioning are well agreed with the experience of operation of spiroid gears and can be applied for analysis of load-carrying capacity of spiroid gears.

This paper presents a method of using association rule data mining algorithms to discover regular sound changes among languages. The method presented has a great potential to facilitate linguistic studies aimed at identifying distantly related cognate languages. As an experimental example, this paper presents the application of the data mining method to the discovery of regular sound changes between the Hungarian and the Sumerian languages, which separated at least five thousand years ago when the Proto-Sumerian reached Mesopotamia. The data mining method discovered an important regular sound change between Hungarian word initial /f/ and Sumerian word initial /b/ phonemes.

Lack of appropriate warehouse ergonomics is one of the leading causes of worker injuries in industry environments. As management teams are trying to reduce the cost of doing business, they look to worker’s health statistics and realize they must improve their way of operating. The paper presents a warehouse analysis done at the request of the management team of a Belgian manufacturing plant. The factory’s warehouse was audited with focus on Kanban bin weight, storage rack height and rack width to assess the ergonomic strain it causes on workers. The paper then presents possible automation options in order to find the best fit to reduce ergonomic impact, while also optimizing the total investment cost.

The paper briefly presents some possibilities of the modern use of cyberspace tools for the theory of artificial intelligence related to the definition of the model and the corresponding possibilities leading to systemic modeling of crises in the modern information and knowledge world and in the area of system security. The modeling process is mainly focused on the specific concept of the virtual model and on the network represented by information and communication systems (ICT) and on the corresponding perspective modeling of the system-based environment used to simulate cyber-attacks and the corresponding levels of cyber-defence. It forms the actual content of the newly conceived modeling with the process of model adaptation in the modern learning environment of artificial intelligence. The paper is based on a scientific task dealing with the possibilities of modeling and adaptation of cybersecurity in a new projected cybersecurity laboratory with the environment of artificial intelligence application in this virtual environment of modern security.

The aim of this paper is to introduce and improve the functionality and reliability of the indoor glass-break detectors for the security application. The most vulnerable part of the house security are the windows which are fragile and very easy to overcome. The glass-break or shock detector are generally used to detect the potential intruder by vibration which is spreading through the glass. The alarm is triggered when a threshold level of the vibration is exceeded. However, the further information of the intrusion is missing. The goal of this paper is a design of a glass-break detector based on the accelerometer which can also detect the vibration in the glass panels with higher accuracy. The main advantage of the implemented accelerometer is the ability to sense several influences of the environment. The program can reliably distinguish between main influences like the rain, storm or strong wind. This function can also be used for the non-alarm application. However, the main function is still focused on the security application. The designed detector should be able to communicate with commercially made Control and Indicating Equipment via the wired connection.

This paper presents an application of Artificial Neural Network models (ANN) to predict the water consumption at two scales: i) District Metered Area (DMA) located in the Scientific Campus of Lille University and ii) End user representing a restaurant inside this DMA. Data are collected from Automated Meter Readers (AMRs) that measure in near real-time the water consumption. The models are trained at both daily and hourly time intervals using historical values and the variation between the hour and the type of days. The paper shows that the ANN-based models can well predict the water consumption including peak values.

Modular design is an important design method in the mass customization for manufacturing industry. The purpose of this paper is to meet diverse market demands while reducing the impact of products on the ecological environment. Firstly, aiming at the product life cycle process, this paper summarizes the problems encountered in each stage of the product, and introduces five green product module partition principles. Then, through the component correlation matrix, the resource greenness objective function based on the whole life cycle and the polymerization degree objective function based on the component correlation matrix are established respectively by the axiomatic design theory which makes the product mapping from functional domain to structural domain. Next, an improved artificial bee colony algorithm is proposed. Based on the artificial bee colony algorithm, the algorithm applies congestion strategy and fast nondominated sorting strategy to solve the module partition problem of product platform with multi-objective optimization, and a uniformly distributed pare to solution set is generated. Through above steps, the optimization results of module partition are obtained. Finally, an application example of aircraft tail horizontal stabilizer parts is given, and the advantages of the algorithm are proved by comparing with other algorithms.

This paper will continue application of neural networks to the concept of environmental quality management technology described elsewhere. Human preferences are different and may vary depending on type of the work or psycho-physical conditions of workers. This paper deals with application of artificial neural networks (ANNs) to control of general lighting systems to provide personal (individual) illuminance on worktables. Two-layer feedforward ANN is used to identify and model the system. Introduction of ANN to model illumination systems opens a new possibility of individual control systems in which specific areas have different set of requirements than remaining areas of a lighting system.

Cancer home care is a sector of particular relevance for the Italian health service. The budget ceilings imposed by public finance pose the need to reduce hospitalisation costs, moving patients from treatment to home care as much as possible. This is especially true in the field of pediatric oncology, where a protected family environment offers a variety of benefits to young patients and their families. However, in order to guarantee adequate assistance services, an integrated information system for management must be devised as the center of gravity of a coordinated network of, even heterogeneous, actors. This paper focuses on the identification and evolving development of web services for regulation and control of home administration of drug infusion in pediatric oncology. The Service Oriented Architecture (SOA) remains the software architecture of reference, where as the methodological approach is open to a reconsideration of the continuous improvement of the whole process in light of the ceaseless progress of medical science and information technology. In this paper, his goal is achieved by using a methodological design approach that combines the UML modeling based on Case Stories and the process optimization of Axiomatic Design. Case Stories allow the formalization of end users' requirements in UML language, easily interpreted by software developers. On the other hand, Axiomatic Design allows optimizing the design process by identifying the most robust solutions in terms of greater logical coherence and lesser systemic complexity. We analyze a case study focused on the design of web services supporting the home administration of drug infusion, for young cancer patients in home care. Then, we show how the UML-modeling methodology can be used to design new services on the basis of specific Use Cases. Finally, through theAxiomatic design approach we verify the proposed solutions, by identifying the robust set of web services to be implemented.