Automation

Financial markets are complex, evolving dynamic systems. Due to their irregularity, financial time series forecasting is regarded as a rather challenging task. In recent years, artificial neural network applications in finance for such tasks as pattern recognition, classification, and time series forecasting have dramatically increased. The objective of this paper is to present this versatile framework and attempt to use it to predict the stock return series of four public-listed companies on the New York Stock Exchange. Our findings coincide with those of Burton Malkiel in his book, A Random Walk Down Wall Street; no conclusive evidence is found that our proposed models can predict the stock return series better than that of a random walk. Full article

Nowadays, academic research, disaster mitigation, industry, and transportation apply the cooperative multi-agent concept. A cooperative multi-agent system is a multi-agent system that works together to solve problems or maximise utility. The essential marks of formation control are how the multiple agents can reach the desired point while maintaining their position in the formation based on the dynamic conditions and environment. A cooperative multi-agent system closely relates to the formation change issue. It is necessary to change the arrangement of multiple agents according to the environmental conditions, such as when avoiding obstacles, applying different sizes and shapes of tracks, and moving different sizes and shapes of transport objects. Reinforcement learning is a good method to apply in a formation change environment. On the other hand, the complex formation control process requires a long learning time. This paper proposed using the Deep Dyna-Q algorithm to speed up the learning process while improving the formation achievement rate by tuning the parameters of the Deep Dyna-Q algorithm. Even though the Deep Dyna-Q algorithm has been used in many applications, it has not been applied in an actual experiment. The contribution of this paper is the application of the Deep Dyna-Q algorithm in formation control in both simulations and actual experiments. This study successfully implements the proposed method and investigates formation control in simulations and actual experiments. In the actual experiments, the Nexus robot with a robot operating system (ROS) was used. To confirm the communication between the PC and robots, camera processing, and motor controller, the velocities from the simulation were directly given to the robots. The simulations could give the same goal points as the actual experiments, so the simulation results approach the actual experimental results. The discount rate and learning rate values affected the formation change achievement rate, collision number among agents, and collisions between agents and transport objects. For learning rate comparison, DDQ (0.01) consistently outperformed DQN. DQN obtained the maximum −170 reward in about 130,000 episodes, while DDQ (0.01) could achieve this value in 58,000 episodes and achieved a maximum −160 reward. The application of an MEC (model error compensator) in the actual experiment successfully reduced the error movement of the robots so that the robots could produce the formation change appropriately. Full article

Radiomics has shown great promise in predicting various diseases. Researchers have previously attempted to include radiomics in their automated detection, diagnosis, and segmentation algorithms, taking these steps based on the promising outcomes of radiomics-based studies. As a result of the increased attention given to this topic, numerous institutions have developed their own radiomics software. These packages, on the other hand, have been utilized interchangeably without regard for their fundamental differences. The primary purpose of this study was to explore benefits of predictive model performance for radiation pneumonitis (RP), which is the most frequent side effect of chest radiotherapy, and through this work, we developed a radiomics model based on deep learning that intends to increase RP prediction performance by combining more data points and digging deeper into these data. In order to evaluate the most popular machine learning models, radiographic characteristics were used, and we recorded the most important of them. The high dimensionality of radiomic datasets is a major issue. The method proposed for use in data problems is the synthetic minority oversampling technique, which we used in order to create a balanced dataset by leveraging suitable hardware and open-source software. The present study assessed the efficacy of various machine learning models, including logistic regression (LR), support vector machine (SVM), random forest (RF), and deep neural network (DNN), in predicting radiation pneumonitis by utilizing specific radiomics features. The findings of the study indicate that the four models displayed satisfactory efficacy in forecasting radiation pneumonitis. The DNN model demonstrated the highest area under the receiver operating curve (AUC-ROC) value, which was 0.87, suggesting its superior predictive capacity among the models considered. The AUC-ROC values for the random forest, SVM, and logistic regression models were 0.85, 0.83, and 0.81, respectively. Full article

This paper presents a quasi-static model-based control algorithm for controlling the motion of a soft robotic exo-digit with three independent actuation joints physically interacting with the human finger. A quasi-static analytical model of physical interaction between the soft exo-digit and a human finger model was developed. Then, the model was presented as a nonlinear discrete-time multiple-input multiple-output (MIMO) state-space representation for the control system design. Input–output feedback linearization was utilized and a control input was designed to linearize the input–output, where the input is the actuation pressure of an individual soft actuator, and the output is the pose of the human fingertip. The asymptotic stability of the nonlinear discrete-time system for trajectory tracking control is discussed. A soft robotic exoskeleton digit (exo-digit) and a 3D-printed human-finger model integrated with IMU sensors were used for the experimental test setup. An Arduino-based electro-pneumatic control hardware was developed to control the actuation pressure of the soft exo-digit. The effectiveness of the controller was examined through simulation studies and experimental testing for following different pose trajectories corresponding to the human finger pose during the activities of daily living. The model-based controller was able to follow the desired trajectories with a very low average root-mean-square error of 2.27 mm in the x-direction, 2.75 mm in the y-direction, and 3.90 degrees in the orientation of the human finger distal link about the z-axis. Full article

For a robot to pick up an object viewed by a camera, the object’s position in the image coordinate system must be converted to the robot coordinate system. Recently, a neural network-based method was proposed to achieve this task. This methodology can accurately convert the object’s position despite errors and disturbances that arise in a real-world environment, such as the deflection of a robot arm triggered by changes in the robot’s posture. However, this method has some drawbacks, such as the need for significant effort in model selection, hyperparameter tuning, and lack of stability and interpretability in the learning results. To address these issues, a method involving linear and nonlinear regressions is proposed. First, linear regression is employed to convert the object’s position from the image coordinate system to the robot base coordinate system. Next, B-splines-based nonlinear regression is applied to address the errors and disturbances that occur in a real-world environment. Since this approach is more stable and has better calibration performance with interpretability as opposed to the recent method, it is more practical. In the experiment, calibration results were incorporated into a robot, and its performance was evaluated quantitatively. The proposed method achieved a mean position error of 0.5 mm, while the neural network-based method achieved an error of 1.1 mm. Full article

Under the new geometric product specification (GPS), a two-dimensional chain cannot completely guarantee quality of the product. To optimize the allocation of three-dimensional tolerances in the conceptual design stage, the geometric variations of the tolerance zone to the deviation domain will be mapped in this paper. The deviation-processing cost, deviation-quality loss cost, and deviation-sensitivity cost function relationships combined with the tolerance zone described by the small displacement torsor theory are discussed. Then, synchronous constraint of the function structure and tolerance is realized. Finally, an improved bat algorithm is used to solve the established three-dimensional tolerance mathematical model. A case study in the optimization of three-part tolerance design is used to illustrate the proposed model and algorithms. The performance and advantage of the proposed method are discussed in the end. Full article

The swift development of autonomous vehicles raises the necessity of semantically mapping the environment by producing distinguishable representations to recognise similar areas. To this end, in this article, we present an efficient technique to cut up a robot’s trajectory into semantically consistent communities based on graph-inspired descriptors. This allows an agent to localise itself in future tasks under different environmental circumstances in an urban area. The proposed semantic grouping technique utilizes the Leiden Community Detection Algorithm (LeCDA), which is a novel and efficient method of low computational complexity and exploits semantic and topometric information from the observed scenes. The presented experimentation was carried out on a novel dataset from the city of Xanthi, Greece (dubbed as $Grypho{n}_{urban}$ urban dataset), which was recorded by RGB-D, IMU and GNSS sensors mounted on a moving vehicle. Our results exhibit the formulation of a semantic map with visually coherent communities and the realisation of an effective localisation mechanism for autonomous vehicles in urban environments. Full article

Land use and land cover (LULC) mapping initiatives are essential to support decision making related to the implementation of different policies. There is a need for timely and accurate LULC maps. However, building them is challenging. LULC changes affect natural areas and local biodiversity. When they cause landscape fragmentation, the mapping and monitoring of changes are affected. Due to this situation, improving the efforts for LULC mapping and monitoring in fragmented biomes and ecosystems is crucial, and the adequate separability of classes is a key factor in this process. We believe that combining multidimensional Earth observation (EO) data cubes and spectral vegetation indices (VIs) derived from the red edge, near-infrared, and shortwave infrared bands provided by the Sentinel-2/MultiSpectral Instrument (S2/MSI) mission reduces uncertainties in area estimation, leading toward more automated mappings. Here, we present a low-cost semi-automated classification scheme created to identify croplands, pasturelands, natural grasslands, and shrublands from EO data cubes and the Surface Reflectance to Vegetation Indexes (sr2vgi) tool to automate spectral index calculation, with both produced in the scope of the Brazil Data Cube (BDC) project. We used this combination of data and tools to improve LULC mapping in the Brazilian Cerrado biome during the 2018–2019 crop season. The overall accuracy (OA) of our results is $88\%$, indicating the potential of the proposed approach to provide timely and accurate LULC mapping from the detection of different vegetation patterns in time series. Full article

With the rise of Industry 4.0, its pillars (which include Internet of Things, “Big Data”, data analytics, augmented reality, cybersecurity, etc.) have become unavoidable tendencies for the automated manufacturing industry. Equipment upgrade is required to match the new standards of digitally assisted automation. However, not all factories in the medium to small range (or independent manufacturers) can afford to upgrade their equipment. Therefore, the availability of affordable Industry 4.0 upgrades for now-outdated devices is necessary for manufacturers in the SME range (Small-Medium Enterprises) to stay relevant and profitable. More specifically, this work revolves around the automation of printed circuit board (PCB) manufacturing, which is one of the most popular and profitable areas involved in this movement; and within it, the large majority of manufacturing defects can be traced to the soldering or “reflow” stage. Manufacturing research must, thus, aim towards improving reflow ovens and, more specifically, aim to improve their autonomous capabilities and affordability. This work presents the design and results of a controlling interface utilizing a Raspberry Pi 4 as a coupling interface between an MQTT Broker (which monitors the overall system) and the oven itself (which is, intentionally, a sub-prime model which lacks native IoT support), resulting in successful, remote, network-based controlling and monitoring of the oven. Additionally, it documents the design and implementation of the network adaptations necessary for it to be considered a cybersecure IIoT Module and connect safely to the Production Cell’s Subnet. All of this to address the inclusion of specific Industry 4.0 needs such as autonomous functioning, data collection and cybersecurity in outdated manufacturing devices and help enrich the processes of SME PCB manufacturers. Full article

This paper provides the theoretical foundation for the approximation of the regions of attraction in hyperbolic and polynomial systems based on the eigenfunctions deduced from the data-driven approximation of the Koopman operator. In addition, it shows that the same method is suitable for analyzing higher-dimensional systems in which the state space dimension is greater than three. The approximation of the Koopman operator is based on extended dynamic mode decomposition, and the method relies solely on this approximation to find and analyze the system’s fixed points. In other words, knowledge of the model differential equations or their linearization is not necessary for this analysis. The reliability of this approach is demonstrated through two examples of dynamical systems, e.g., a population model in which the theoretical boundary is known, and a higher-dimensional chemical reaction system constituting an original result. Full article

A proxy war between a coalition of countries, BLUE, and a country, RED, is considered. RED wants to increase the size of the RED territory. BLUE wants to involve more regions in trade and other types of cooperation. GREEN is a small and independent nation that wants to become a member of BLUE. RED attacks GREEN and tries to invade. BLUE decides to give optimal arms support to GREEN. This support can help GREEN in the war against RED and simultaneously can reduce the military power of RED, which is valuable to BLUE also outside this proxy war, since RED may confront BLUE also in other regions. The optimal control problem of dynamic arms support, from the BLUE perspective, is defined in general form. From the BLUE perspective, there is an optimal position of the front. This position is a function of the weights in the objective function and all other parameters. Optimal control theory is used to determine the optimal dynamic BLUE strategy, conditional on a RED strategy, which is observed by BLUE military intelligence. The optimal arms support strategy for BLUE is to initially send a large volume of arms support to GREEN, to rapidly move the front to the optimal position. Then, the support should be almost constant during most of the war, keeping the war front location stationary. In the final part of the conflict, when RED will have almost no military resources left and tries to retire from the GREEN territory, BLUE should strongly increase the arms support and make sure that GREEN rapidly can regain the complete territory and end the war. Full article

Automation and control systems are constantly evolving, using artificial intelligence techniques to implement new forms of control, such as fuzzy control, with advantages over classic control strategies, especially in non-linear systems. Water supply networks are complex systems with different operating configurations, uninterrupted operation requirements, equalization capacity and pressure control in the supply networks, and high reliability. In this sense, this work aims to develop a fuzzy pressure control system for a supply system with three possible operating configurations: a single motor pump, two motor pumps in series, or two motor pumps in parallel. For each configuration, an energy efficiency analysis was carried out according to the demand profile established in this case study. In order to validate the proposed methodology, an experimental water supply system was used, located in the Laboratory of Energy Efficiency and Hydraulics in Sanitation at the Federal University of Paraiba (LENHS/UFPB). Full article

The aim of the presented feasibility study was to systematically investigate the automation of the skimming (i.e., removal) of zinc ash from the surface of the zinc bath in order to minimise the risks for workers due to mechanical hazards (risk of falling into the zinc kettle) and chemical hazards (inhalation exposure to zinc vapours) by eliminating this activity. As part of the feasibility study, automatic separation and skimming systems from various applications, such as removal systems of slags and metal foam, were identified. For this purpose, their technical feasibility and suitability were considered. Two automated techniques, a mechanical and a gas-based skimming system, were selected for the subsequent laboratory-based evaluation. In the scope of the practical feasibility study, the selected skimming techniques were designed, constructed, and evaluated based on near-process prototype tests on a laboratory scale. The focus was on the efficiency of the skimming systems, related to the removal of zinc ash from the free surface of the molten zinc (general efficiency), as well as to the zinc ash removal with a simulated attachment system of the samples to be galvanised (task-related efficiency). The desired complete removal of zinc ash from the zinc bath surface was demonstrated with two automated methods: a pulse wave method of the mechanical skimming system and a gas-based skimming system in general, operating independently from the attachment system. Additionally, as part of the process-related simulation of the complete batch galvanising process, a fully automated combination of the zinc ash skimming and extraction system was achieved on a laboratory scale. Full article

Starting from the problem of studying the parametric robustness in the case of the control of a permanent magnet-synchronous motor (PMSM), although robust control systems correspond entirely to this problem, due to the complexity of the algorithms of the robust type, in this article the use of switched systems theory is proposed as a study option, given the fact that these types of systems are suitable both for the study of systems with variable structure and for systems with significant parametric variation under conditions of lower complexity of the control algorithms. The study begins by linearizing a PMSM model at a static operating point and continues with a systematic presentation of the basic elements and concepts concerning the stability of switched systems by applying these concepts to the control system of a PMSM based on the field-oriented control (FOC) strategy, which usually changes the value of its parameters during operation (stator resistance R_s, stator inductances L_d and L_q, but also combined inertia of PMSM rotor and load J). The numerical simulations performed in Simulink validate the fact that, for parametric variations of the PMSM structure, the PMSM control switched systems preserve qualitative performance in terms of its control. A series of Matlab programs are presented based on the YALMIP toolbox to obtain P_i matrices, by solving Lyapunov–Metzler type inequalities, and using dwell time to demonstrate stability, as well as the qualitative study of the performance of PMSM control switched systems by presenting in phase plane and state space analysis of the evolution of state vectors: ω PMSM rotor speed, i_q current, and i_d current. Full article

Vessel motion simulation as well as model-based accurate trajectory prediction of vessels require accurate models with respect to related dynamic properties. The ability to predict vessel’s trajectory behaviors will become relevant in the case of future autonomous navigation of vessels to predict the behavior of others. The definition of models or parameters can be realized via first principles or by using experimental modeling methods leading to a time invariant or variant model. Existing hydrodynamical modeling approaches are based on mathematical approaches, which use parameters like mass, hydrodynamic forces, wind velocity, depth under the keel, loading parameters, etc. So, determining a dynamic vessel’s model is a complex task, since the model is vessel-specific. For collision avoidance of autonomous or assisted vessels, the trajectory prediction of encountering other vessels is especially required. It is not possible to use complex hydrodynamical models of encountering vessels online due to missing required information/measurements. Even existing deep learning approaches provide better predictions, but are still insufficient for collision avoidance in the case of strong dynamical changes, since the considered input sequences are long. Due to long input sequences, the model does not adapt to strong dynamical changes. In this work, a simple parameter-based approach is developed to predict the intended behavior using the last seconds of the measured position variables. The idea is to globally identify the model parameters of the vessel, which remains constant for the situation, and additionally two parameters for local adaptation, which adapt at every updated input sequence. Typically parameters like rudder angle, wind velocities, and water current affect the behavior of vessels. The introduced approach works with a sliding window approach for which, after identification of the global system, local values are identified based on the last 80 measurements of the vessels. A trajectory prediction (assuming no additional rudder-based maneuvering) is realized for the prediction horizon of 180 s. To confirm the robustness of the new approach, real AIS/GPS-based measurements from a German research inland vessel for different scenarios and sailing conditions including ‘loaded’ and ‘empty’ sailing cases are used. Furthermore, additional results are shown for position data information of different sample rates. Full article

Force myography (FMG) detects hand gestures based on muscular contractions, featuring as an alternative to surface electromyography. However, typical FMG systems rely on spatially-distributed arrays of force-sensing resistors to resolve ambiguities. The aim of this proof-of-concept study is to develop a method for identifying hand poses from the static and dynamic components of FMG waveforms based on a compact, single-channel optical fiber sensor. As the user performs a gesture, a micro-bending transducer positioned on the belly of the forearm muscles registers the dynamic optical signals resulting from the exerted forces. A Raspberry Pi 3 minicomputer performs data acquisition and processing. Then, convolutional neural networks correlate the FMG waveforms with the target postures, yielding a classification accuracy of (93.98 ± 1.54)% for eight postures, based on the interrogation of a single fiber transducer. Full article

Programmable logic controllers (PLCs) make up a substantial part of critical infrastructures (CIs) and industrial control systems (ICSs). They are programmed with a control logic that defines how to drive and operate critical processes such as nuclear power plants, petrochemical factories, water treatment systems, and other facilities. Unfortunately, these devices are not fully secure and are prone to malicious threats, especially those exploiting vulnerabilities in the control logic of PLCs. Such threats are known as control logic injection attacks. They mainly aim at sabotaging physical processes controlled by exposed PLCs, causing catastrophic damage to target systems as shown by Stuxnet. Looking back over the last decade, many research endeavors exploring and discussing these threats have been published. In this article, we present a flashback on the recent works related to control logic injection attacks against PLCs. To this end, we provide the security research community with a new systematization based on the attacker techniques under three main attack scenarios. For each study presented in this work, we overview the attack strategies, tools, security goals, infected devices, and underlying vulnerabilities. Based on our analysis, we highlight the current security challenges in protecting PLCs from such severe attacks and suggest security recommendations for future research directions. Full article

The ideal smart home could be automatically controlled using a variety of electronic tools and devices to perform everyday tasks. Smart home automation is crucially beneficial for human life, particularly when considering those with disabilities, inpatients, and elderly populations. In this paper, applications and systems for smart homes are investigated. During experimentation they were controlled via an Android mobile phone and the Arduino platform. Bluetooth Module HC-06 was used to connect the Arduino Uno R3 with the mobile phone. Five smart home applications were developed to control the lighting and electrical sockets, fan speed, temperature- and humidity-meter display/controls, as well as the fire-alarm and toxic-gas alarm systems. Herein, the definition, the graphical user interface, the required main components, and the control circuit connections are prepared and presented for each application. The graphical user interface was created using the RemoteXY website, which is a reliable website for this purpose. The developed applications were tested, and they were found to work efficiently and correctly. Additionally, this innovative system is both cost-effective and affordable (total cost at the time of development was 110 USD). Full article