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Beragam peralatan listrik maupun elektronik yang beroperasi dengan tegangan searah menggunakan baterai sebagai sumber energi listriknya. Sebagian besar baterai tersebut dapat diisi ulang dengan charger sehingga dapat digunakan dalam jangka waktu yang lama. Pengisian baterai dengan charger perlu dikendalikan dan dimonitor agar tidak terjadi sesuatu yang tidak diinginkan seperti adanya kasus overcharging pada baterai. Paper ini mengusulkan sistem kendali dan monitoring charger baterai dengan menggunakan panel surya sebagai sumber daya listriknya. Sistem kendali dan monitoring charger baterai ini dirancang dengan menggunakan Programable Logic Controller (PLC) S7 1200 1215C DC/DC/Relay dan Human Machine Interface (HMI) Simatic KTP 700 Basic PN untuk display dari parameter yang dimonitor, yakni arus, tegangan dan State of Charge (SOC) dari baterai. Sistem kendali dilengkapi dengan solar charger, sensor tegangan, sensor arus dan relay sebagai pemutus rangkaian charger ketika pengisian baterai telah penuh.Sistem kendali dan monitoring charger baterai yang diusulkan ini diimplementasikan pada dua buah baterai 100 Ah 12 Volt yang disuplai dengan delapan buah panel surya 50 WP. Hasil eksperimen menunjukan bahwa sistem kendali dan monitoring baterai yang diusulkan telah bekerja dengan baik sesuai dengan tujuan. HMI sudah berhasil menampilkan data tegangan, arus dan SOC baterai dan juga telah sukses mengendalikan charger baterai agar tidak terjadi overcharging.

Nowadays, the growth in the consumption of energy and the need to face pollution resulting from its generation are causing concern for consumers and providers. Energy consumption in residential buildings and houses is about 22% of total energy production. Cutting-edge energy managers aim to optimize electrical devices in homes, taking into account users’ patterns, goals, and needs, by creating energy consumption awareness and helping current change habits. In this way, energy manager systems (EMSs) monitor and manage electrical appliances, automate and schedule actions, and make suggestions regarding electrical consumption. Furthermore, gamification strategies may change energy consumption patterns through energy managers, which are seen as an option to save energy and money. Therefore, this paper proposes a personalized gamification strategy for an EMS through an adaptive neuro-fuzzy inference system (ANFIS) decision-making engine to classify the level of electrical consumption and persuade the end-user to reduce and modify consumption patterns, saving energy and money with gamified motivations. These strategies have proven to be effective in changing consumer behavior with intrinsic and extrinsic motivations. The interfaces consider three cases for summer and winter periods to calculate the saving-energy potentials: (1) for a type of user that is interested in home-improvement efforts while helping to save energy; (2) for a type of user that is advocating to save energy; (3) for a type of user that is not interested in saving energy. Hence, each interface considers the end-user’s current consumption and the possibility to modify their consumption habits using their current electrical devices. Finally, an interface displaying the electrical consumption for each case exemplifies its linkage with EMSs.

Depression is a common mental illness characterized by sadness, lack of interest, or pleasure. According to the DSM-5, there are nine symptoms, from which an individual must present 4 or 5 in the last two weeks to fulfill the diagnosis criteria of depression. Nevertheless, the common methods that health care professionals use to assess and monitor depression symptoms are face-to-face questionnaires leading to time-consuming or expensive methods. On the other hand, smart homes can monitor householders' health through smart devices such as smartphones, wearables, cameras, or voice assistants connected to the home. Although the depression disorders at smart homes are commonly oriented to the senior sector, depression affects all of us. Therefore, even though an expert needs to diagnose the depression disorder, questionnaires as the PHQ-9 help spot any depressive symptomatology as a pre-diagnosis. Thus, this paper proposes a three-step framework; the first step assesses the nine questions to the end-user through ALEXA or a gamified HMI. Then, a fuzzy logic decision system considers three actions based on the nine responses. Finally, the last step considers these three actions: continue monitoring through Alexa and the HMI, suggest specialist referral, and mandatory specialist referral.

As the number of electronic gadgets in our daily lives is increasing and most of them require some kind of human interaction, this demands innovative, convenient input methods. There are limitations to state-of-the-art (SotA) ultrasound-based hand gesture recognition (HGR) systems in terms of robustness and accuracy. This research presents a novel machine learning (ML)-based end-to-end solution for hand gesture recognition with low-cost micro-electromechanical (MEMS) system ultrasonic transducers. In contrast to prior methods, our ML model processes the raw echo samples directly instead of using pre-processed data. Consequently, the processing flow presented in this work leaves it to the ML model to extract the important information from the echo data. The success of this approach is demonstrated as follows. Four MEMS ultrasonic transducers are placed in three different geometrical arrangements. For each arrangement, different types of ML models are optimized and benchmarked on datasets acquired with the presented custom hardware (HW): convolutional neural networks (CNNs), gated recurrent units (GRUs), long short-term memory (LSTM), vision transformer (ViT), and cross-attention multi-scale vision transformer (CrossViT). The three last-mentioned ML models reached more than 88% accuracy. The most important innovation described in this research paper is that we were able to demonstrate that little pre-processing is necessary to obtain high accuracy in ultrasonic HGR for several arrangements of cost-effective and low-power MEMS ultrasonic transducer arrays. Even the computationally intensive Fourier transform can be omitted. The presented approach is further compared to HGR systems using other sensor types such as vision, WiFi, radar, and state-of-the-art ultrasound-based HGR systems. Direct processing of the sensor signals by a compact model makes ultrasonic hand gesture recognition a true low-cost and power-efficient input method.

Automation transparency offers a promising way for users to understand the uncertainty of automated driving systems (ADS) and to calibrate their trust in them. However, not all levels of information may be necessary to achieve transparency. In this study, we conceptualized the transparency of the automotive human–machine interfaces (HMIs) in three levels, using driving scenarios comprised of two degrees of urgency to evaluate drivers’ trust and reliance on a highly automated driving system. The dependent measures included non-driving related task (NDRT) performance and visual attention, before and after viewing the interface, along with the drivers’ takeover performance, subjective trust, and workload. The results of the simulated experiment indicated that participants interacting with an SAT level 1 + 3 (system’s action and projection) and level 1 + 2 + 3 (system’s action, reasoning, and projection) HMI trusted and relied on the ADS more than did those using the baseline SAT level 1 (system’s action) HMI. The low-urgency scenario was associated with higher trust and reliance, and the drivers’ visual attention and NDRT performance improved after viewing the HMI, but not statistically significantly. The findings verified the positive role of the SAT model regarding human trust in the ADS, especially in regards to projection information in time-sensitive situations, and these results have implications for the design of automotive HMIs based on the SAT model to facilitate the human–ADS relationship.

The increasing level of intelligence in automobiles is driving a shift in the human–machine relationship. Users are paying more attention to the intelligent cabin and showing a tendency toward customization. As culture is considered to be an important factor in guiding user behavior and preference, this study innovatively incorporates cultural and human factors into the model to understand how individual cultural orientation influences user preference for innovative human-machine interaction (HMI) features. Firstly, this study considered five Hofstede cultural dimensions as potential impact factors and constructed a prediction model through the random forest algorithm so as to analyze the influence mechanism of culture. Subsequently, K-means clustering was used to classify the sample into three user groups and then predict their preferences for the innovative features in the intelligent cabin. The results showed that users with a higher power distance index preferred a sense of ceremony and show-off-related features such as ambient lighting and welcome mode, whereas users with high individualism were keen on a more open and personalized in-vehicle information system. Long-term orientation was found to be associated with features that help to improve efficiency, and users with a lower level of uncertainty avoidance and restraint were more likely to be attracted to new features and were also more willing to use entertainment-related features. The methodology developed in this study can be widely applied to people in different countries, thus effectively exploring the personal requirements of different individuals, guiding further user experience design and localization when breaking into a new market.

The article focuses on the design considerations for achieving a luxury driver experience in automotive Human-Machine Interface (HMI) and interiors, particularly in the context of interactive technologies and user experiences. Topics include the elements of luxury in car design, the challenges faced by designers, and the potential transferability of design considerations across different cultures and markets.

Introduction: SAE Level 3 is known as conditional driving automation. As long as certain conditions are met, there is no need to supervise the technology and the driver can engage in non-driving related tasks (NDRTs). However, a human driver must be present and alert to take over when the automation is facing its system limits. When such an emergency takes place, the automation uses the human machine interface (HMI) to send a take-over request (TOR) to the driver. Methods: We investigated the influence of a color themed HMI on the trust and take-over performance in automated vehicles. Using a driving simulator, we tested 45 participants divided in three groups with a baseline auditory HMI and two advanced color themed HMIs consisting of a display and ambient lighting with the colors red and blue. Trust in automation was assessed using questionnaires while take-over performance was assessed through response time and success rate. Results: Compared to the baseline HMI, the color themed HMI is more trustworthy, and participants understood their driving tasks better. Results show that the color themed HMI is perceived asmore pleasant compared to the baseline HMI and leads to shorter reaction times. Red ambient lighting is seen as more urging than blue, but HMI color did not significantly affect the general HMI perception and TOR performance. Discussion: Further research can explore the use of color and other modalities to express varying urgency levels and validate findings in complex on road driving conditions. [ABSTRACT FROM AUTHOR]

Today is the era of ultra-age technology and practices for the betterment of the society. Drone is the Unmanned Aerial Vehicle (UAV), which needs a path planning to reach up to the target. There are two basic modes for use of drone in case of military/surveillance: first is attack mode and defensive mode. Hence, this paper focuses on defensive mode as a scope of the proposed study. This paper provides significance of drone surveillance, a new artificial intelligence strategy to develop a predictive model based on the path planning. Further, based on the drone dataset, the UAV travel graph can be predicted and tested with a recursive machine learning algorithm. This strategy can be clubbed as an image path using deep learning algorithm also but to ensure the graph-based training and testing, the proposed research will use CNN algorithm for comparative analysis of simulated path's plan coordinates. This further can be developed as a human-machine interface module. [ABSTRACT FROM AUTHOR]

Ergonomic reliability plays a significant role in the safe operation of devices.With the spread of infectious diseases around the world, in work environments with high loads and high infection rates, medical staff work in a state of high self-protection. The use of visual display terminal (VDT) for medical equipment has undergone fundamental changes, and the traditional medical equipment human-machine interface design needs to be improved. After the completion of design and development, a VDT design enters the experimental testing stage, which has significant limitations for simulating the work of medical staff in the high-load and high-infection environments. The testing cost is high, and subjects face harsh conditions; thus, an ergonomic reliability model that can predict the use of VDT in such special high-infection and high-load circumstances must be established. An ergonomic reliability model based on an improved backpropagation neural network (BPNN) and human cognition reliability (HCR) is proposed for predicting and evaluating operation flows according tomedical equipment VDTs. Firstly, a small data sample can be used to train BPNN to generate a network that can ensure suitable accuracy. To prevent the model from falling into local optimal solutions, the bat algorithm is introduced to improve the BPNN. Compared to a traditional BPNN, the superiority of the improved BPNN is clearly demonstrated. Secondly, the HCR method is used to analyze and highlight changes in the human factor reliability of VDTs for medical equipment in different time processes and operating processes according to BPNN prediction results, to provide a reference for selecting the optimalmethod. Finally, the validity and availability of the proposedmethod are verified through an eye tracker experiment and statistical analysis results. [ABSTRACT FROM AUTHOR]

The objective of this work is to mathematically model vehicular connectivity using ALOHA base function with modified logistic function as an adaptive control function. The focus of this work is to utilize the ALOHA protocol as a carrier sensing multiple access technique and add to it an intelligent part, which is the modified logistic function (MLF) to enable adaptive communication channel. The work correlates three time based parameters related to vehicle, communication, and human driver, which are associated with connected vehicles (driver response time, machine processing time, and channel communication time). The mathematical model showed that throughput for autonomous and connected vehicles is much higher than that for connected vehicles due to the elimination of human time factor, together with the evident low response time from sensors, electronics, and communication. In addition, a more stable connectivity is established if human element is not part of the model, as this eliminates both uncertainty and lengthy human response time. The ALOHA function role is to enable continuous tries by each node (vehicle) to establish communication with probability related to availability of communication channel and a back off functionality in case of collision. The proposed model provides dynamic and adaptive technique to adjust time slots using the continuous change in the vehicular connectivity environment, which affects the presented time parameters, thus changing frames delivery pattern and time slots utilization. The presented approach, supports better and more efficient use of communication channel, particularly in the case of autonomous and connected vehicles. It also, highlights through comparison the inhibitory effect driver actions can have on safe driving, through time delay, and low communication channel utilization. In addition, the work also focusses on the effect of processing time for sensors and electronics, which also have negative effect on channel usage and effectiveness, if there is a functional problem. [ABSTRACT FROM AUTHOR]

In the case of the cooperative intelligent transportation system (C-ITS), one of the advanced technologies to improve the drivability of existing vehicles and driver safety, including commercial vehicles, we need additional consideration of the C-ITS service at the driver level. In particular, it is necessary to identify and improve C-ITS functions and service issues at the HMI level in advance through scenario development and driver reaction simulation. In this paper, we developed C-ITS driver HMI scenarios. For the development, we first investigated the current C-ITS service and in-vehicle HMI analysis for driver scenarios. We also measured the driver's reaction speed using Simio simulation. Given our analysis, we developed service blueprinting maps for commercial vehicle drivers, including user journeys, and we analyzed C-ITS services from various user HMI perspectives. [ABSTRACT FROM AUTHOR]

This paper treats for automotive application the trade‐off between power saving and visual quality enhancement at one side and logic complexity as well as processing speed of local dimming processor at the other side. A key parameter is the resolution of an image condensed which is effectively the input image for the LED optimization. A higher number of condensation cells allows a more detailed consideration of the image given and the shape of the light spread function (LSF) of an LED. On the other hand, more memories are needed, and the processing steps are increased. The number of condensations cells are varied for 3 backlight units (BLU) with 133, 240 and 384 LEDs for a standard automotive display size of 12.3 inches. Power saving rate for a set of automotive HMIs as well as contrast enhancement are calculated. A recommendation for the resolution of image condensation in dependence of the LED resolution of the BLU will be given. [ABSTRACT FROM AUTHOR]

Penelitian ini bertujuan untuk memudahkan dalam penimbangan sebuah objek dengan tampilan data digital menggunakan outseal PLC sebagai pusat kontrol. Penimbangan yang dilakukan dengan konvensional memerlukan pembacaan hasil yang berulang sehingga membutuhkan waktu lama dan hasil yang bervariasi disebabkan oleh faktor luar seperti pembacaan yang tidak tepat akibat sudut pandang yang berbeda. Dengan adanya timbang digital akan memudahkan pembacaan nilai dari hasil yang ditimbang. Metode yang digunakan dalam penelitian yaitu metode eksperimen dimana pada penelitian terdiri dari perancangan hardware, elektrikal, dan software. Dalam penelitian, outseal PLC yang berfungsi sebagai pusat kontrol. Input yang digunakan yaitu sensor load cell untuk mendeteksi berat. HMI weintek MT8071iP untuk membaca hasil penimbangan secara digital. Serial komunikasi antara HMI dengan outseal PLC menggunakan serial RS485. Pada penelitian ini dilakukan dua macam pengujian, pertama pengujian penimbangan berat yang berbeda dengan mengukur tegangan output load cell dari setiap massa yang ditimbang. kedua, Pengujian penimbangan dengan berat yang sama sebanyak sepuluh kali percobaan. Objek yang digunakan dalam pengujian timbangan digital ini yaitu granule cat litter yang diletakan dalam sebuah wadah. Dari pengujian pertama didapatkan hasil grafik perbandingan antara tegangan keluaran load cell dengan nilai berat yang ditimbang. Pada pengujian, didapatkan hasil pengukuran yang memiliki akurasi yang tinggi dan kesalahan yang rendah. Pada pengujian dari hasil penimbangan dengan berat yang sama sebanyak sepuluh kali percobaan didapatkan error rata rata 0.04% dan tingkat akurasi 99.96%.

Interdigitated capacitive (IDC) sensors have been extensively researched for human-machine interfaces (HMIs) due to consistent response and easy manufacturing. However, the manual fabrication process and single mode of operation, like buttons, limit their use for future developments of HMIs. We reported a maskless laser lithography-based fabrication process for developing chromium metal electrodes on glass. Chromium metal fabricates sensors on a glass substrate because of its durability. IDC touch sensors are sensitive to the pressure of human finger touch to perform multimode of operation intelligently by distinguishing between no touch, low-pressure touch (LPT) and high-pressure touch (HPT). Sensors exhibit ~ 25 pF/N sensitivity for the force of finger touch, 0.4-sec response, recovery time and durability > 20,000 tests. Touch sensors are arrayed to create pressure-sensitive multimode HMIs for wireless and intelligent mouse cursor movement (MCM) control, numeric keyboard, and smart door locking (SDL) system. Six capacitive pressure sensitive (CPS) sensors-based HMIs utilized to control the low and high speed of the mouse cursor in all four directions and perform the left and right click operations. Numeric keyboard of eight CPS sensors is developed to respond wirelessly to numbers and special characters to a laptop. SDL system demonstrated using the CPS HMI consists of numeric sensors from 0-9, reset and done sensors to utilize it for security applications. HMI sensors responded numerically (0-9) for LPT and special characters for HPT. An algorithm is developed to operate the HMI for the SDL system and use special characters along with numeric, which immensely increases the strength of passwords for security applications. With daily life using substrate material glass, multimode of operation, easily arrayed, and customization, the presented HMIs have the potential to contribute to future developments in intelligent user interfaces, portable computer peripherals, security applications, ATMs, and smart home systems.

There are some vital loads and must not extinguished on the production activities of PT. Semen Padang cause the operational costs to rerun are very large. To solve this problem, it is necessary to evaluate the coordination settings of protection relays and automatic control systems to supporting the production process of PT. Semen Padang so production efficiency can be maintained. The relay settings to be evaluated are inverse time and instantaneous overcurrent relays by coordinating the main relay, backup relay 1 and backup relay 2 from downstream to upstream load feeder. The designed control system is an automatic load shedding and HMI display that can visualizing the system in realtime using the PLC Siemens S7-1500 with simulating on the PLCSimulator of the TIA Portal 15.1 software. Based on the calculations carried out, there are significant differences in the relay settings from the calculation results with the existing state. the relays installed in the system do not coordinate properly in the existing state, while after the calculation of the coordination relays the relays coordinate well. From the design of the control system, an automatic load shedding program and the display of the HMI system in real time were produced.

The servo motor control and monitoring system based on the human machine interface is a system that can control and monitor the speed of the servo motor. Servo motors can be controlled and monitored because of the mutual communication between the motor driver and the encoder that translates and controls the servo motor. The motor driver works on the command of a programmable logic controller that has been programmed with the help of a computer. For control and monitoring, the human machine interface is used as a tool that can interact with the user as a controller as well as monitoring the speed and position of the servo motor. The hardware used is the SIEMENS SIMATIC 1215C DC/DC/DC programmable logic controller, SIMATIC BASIC COMFORT human machine interface, SINAMICS V90 motor driver and SIMOTICS S-1FL6 servo motor. TIA PORTAL and V-Assistant software are used for programming and parameter setting of each system hardware.

The blending of human and mechanical capabilities has become a reality in the realm of Industry 4.0. Enterprises are encouraged to design frameworks capable of harnessing the power of human and technological resources to enhance the era of Artificial Intelligence (AI). Over the past decade, AI technologies have transformed the competitive landscape, particularly during the pandemic. Consequently, the job market, at an international level, is transforming towards the integration of suitably skilled people in cutting edge technologies, emphasizing the need to focus on the upcoming super-smart society known as Society 5.0. The concept of a Humachine builds on the notion that humans and machines have a common future that capitalizes on the strengths of both humans and machines. Therefore, the aim of this paper is to identify the capabilities and distinguishing characteristics of both humans and machines, laying the groundwork for improving human–machine interaction (HMI). [ABSTRACT FROM AUTHOR]

In this paper, we present our investigation of the 2D Hand Gesture Recognition (HGR) which may be suitable for the control of the Automated Guided Vehicle (AGV). In real conditions, we deal with, among others, a complex background, changing lighting conditions, and different distances of the operator from the AGV. For this reason, in the article, we describe the database of 2D images created during the research. We tested classic algorithms and modified them by us ResNet50 and MobileNetV2 which were retrained partially using the transfer learning approach, as well as proposed a simple and effective Convolutional Neural Network (CNN). As part of our work, we used a closed engineering environment for rapid prototyping of vision algorithms, i.e., Adaptive Vision Studio (AVS), currently Zebra Aurora Vision, as well as an open Python programming environment. In addition, we shortly discuss the results of preliminary work on 3D HGR, which seems to be very promising for future work. The results show that, in our case, from the point of view of implementing the gesture recognition methods in AGVs, better results may be expected for RGB images than grayscale ones. Also using 3D imaging and a depth map may give better results. [ABSTRACT FROM AUTHOR]

The article deals with the design of a PLC control system for controlling cascade tanks in the technological process of liquid mixing. The mixing process is carried out automatically according to information from individual sensors. The system also includes elements for manual control of the technological system. A human machine interface system was designed to operate the system. The simulation showed the correctness of the designed system. [ABSTRACT FROM AUTHOR]

This work aims to investigate the effect of driver response to relayed messages through Human Machine Interface (HMI) on the effectiveness of Human-Vehicle Interface (HVI) and to enable the optimized design of HMI. The investigation and mathematical modeling cover vehicles with Advanced Vehicle Assistant System (ADAS), which operates using Advanced Vehicle Control System (AVCS). The presented model uses driver response time and machine (electronics, sensors, processors) processing time to measure vehicular efficiency and driver interaction, which is also a function of the HMI design, and the way messages are passed to the driver. The produced model uses a probability function that can be used in the design and testing process to assess the effect of failure on the designed interface and relates the function to the driver's response time ratio and the vehicle electronics' processing time. The presented work concluded that as the driver response time increases, effective interaction decreases as a probability function. Also, as the driver response time deviates from the specified threshold, the effective interaction decreases, which also applies to the processing time. In addition, as the time ratio between the driver responses to the machine processing (Time Ratio) increases, the effective interaction parameter (R) value decreases. The work also proved that a more adaptive model is possible using a probability function correlated to the response time ratio to processing time. [ABSTRACT FROM AUTHOR]

Eskalator Otomatis Merupakan suatu alat transportasi vertical yang dapat memindahkan satu orang ataupun lebih, dari lantai dasar menuju ke lantai atas atau berikutnya maupun sebaliknya, Tidak jarang pengguna Eskalator mengabaikan keselamatan mereka dengan tidak memperhatikan & keselamatan. Banyaknya kasus kecelakaan yang terjadi di eskalator terutama pada anak-anak yang disebabkan oleh kelalaian dan juga kurangnya safety system yang ada pada escalator. Warning & Safety Sistem pada Eskalator otomatis merupakan suatu sistem pengaman pada eskalator otomatis berbasis PLC (Programmable Logic Controller), sebagai control utama yang dirancang untuk mengantisipasi terjadinya kecelakaan pada penggunaan Eskalator, dan HMI (Human Machine Interface), sebagai media monitoring kerja dari system yang telah dirancang. Sensor Photoelectric ditanamkan pada sistem ini berfungsi sebagai input ke PLC sekaligus sebagai saklar dari warning & safety system pada eskalator yang kemudian sinyal input dari sensor tersebut diolah oleh PLC. Output yang dihasilkan berupa sinyal untuk menggerakan pada motor reverse dan bunyi dari sistem peringatan. Dengan adanya alat ini memanfaatkan sensor Photoelectrik yang diatur jaraknya dari 5 cm, 10 cm, 20 cm, 40 cm dan 80 cm dimana jika sensor off maka tegangan output bernilai 0 VDC dan jika on maka tegangan v output bernilai 5,03 VDC.

IntroductionSAE Level 3 is known as conditional driving automation. As long as certain conditions are met, there is no need to supervise the technology and the driver can engage in non-driving related tasks (NDRTs). However, a human driver must be present and alert to take over when the automation is facing its system limits. When such an emergency takes place, the automation uses the human machine interface (HMI) to send a take-over request (TOR) to the driver.MethodsWe investigated the influence of a color themed HMI on the trust and take-over performance in automated vehicles. Using a driving simulator, we tested 45 participants divided in three groups with a baseline auditory HMI and two advanced color themed HMIs consisting of a display and ambient lighting with the colors red and blue. Trust in automation was assessed using questionnaires while take-over performance was assessed through response time and success rate.ResultsCompared to the baseline HMI, the color themed HMI is more trustworthy, and participants understood their driving tasks better. Results show that the color themed HMI is perceived as more pleasant compared to the baseline HMI and leads to shorter reaction times. Red ambient lighting is seen as more urging than blue, but HMI color did not significantly affect the general HMI perception and TOR performance.DiscussionFurther research can explore the use of color and other modalities to express varying urgency levels and validate findings in complex on road driving conditions.

The effects of climate change and the rapid growth of societies often lead to water scarcity and inadequate water quality, resulting in a significant number of diseases. The digitalization of infrastructure and the use of Digital Twins are presented as alternatives for optimizing resources and the necessary infrastructure in the water cycle. This paper presents a framework for the development of a Digital Twin platform for a wastewater treatment plant, based on a microservices architecture which optimized its design for edge computing implementation. The platform aims to optimize the operation and maintenance processes of the plant’s systems, by employing machine learning techniques, process modeling and simulation, as well as leveraging the information contained in BIM models to support decision-making.

The design and functionality of the human–machine interface (HMI) significantly affects operational efficiency and safety related to process control. Alarm management techniques consider the cognitive model of operators, but mainly only from a signal perception point of view. To develop a human-centric alarm management system, the construction of an easy-to-use and supportive HMI is essential. This work suggests a development method that uses machine learning (ML) tools. The key idea is that more supportive higher-level HMI displays can be developed by analysing operator-related events in the process log file. The obtained process model contains relevant data on the relationship of the process events, enabling a network-like visualisation. Attributes of the network allow us to solve the minimisation problem of the ideal workflow–display relation. The suggested approach allows a targeted process pattern exploration to design higher-level HMI displays with respect to content and hierarchy. The method was applied in a real-life hydrofluoric acid alkylation plant, where a proposal was made about the content of an overview display.

Human–Machine Interface (HMI) plays a key role in the interaction between people and machines, which allows people to easily and intuitively control the machine and immersively experience the virtual world of the meta-universe by virtual reality/augmented reality (VR/AR) technology. Currently, wearable skin-integrated tactile and force sensors are widely used in immersive human–machine interactions due to their ultra-thin, ultra-soft, conformal characteristics. In this paper, the recent progress of tactile and force sensors used in HMI are reviewed, including piezoresistive, capacitive, piezoelectric, triboelectric, and other sensors. Then, this paper discusses how to improve the performance of tactile and force sensors for HMI. Next, this paper summarizes the HMI for dexterous robotic manipulation and VR/AR applications. Finally, this paper summarizes and proposes the future development trend of HMI. [ABSTRACT FROM AUTHOR]

• An HMI creating mode awareness during automated driving is developed and evaluated. • Functional requirements, based on literature and 5 experiments, were implemented. • Next steps were set using VR and RITE (Rapid Iterative Testing and Evaluation) • The final continuous and holistic design creates mode awareness through ambience. • Findings show that the final design was comprehended well and had a good usability. With increasing implementation of automated driving technology it is expected that different automation modes will be present within the same vehicle and within a single trip. At all times during automated driving the driver needs to have 'mode awareness', which is an understanding of the automation mode and the corresponding responsibilities. Yet, research on HMI design to support mode awareness for multiple automation modes within a single vehicle and within a single trip is currently limited. The current work describes the development and evaluation of a Human Machine Interface (HMI) to support mode awareness while driving in different automation modes. The work exists of three phases: Phase 1 defines functional requirements for HMI design based on literature review and 5 experimental studies including 146 participants. Phase 2 implements the functional requirements in HMI design through expert and focus group sessions. Phase 3 evaluates and improves upon the HMI design employing virtual reality and the RITE (Rapid Iterative Testing and Evaluation) method with 18 participants. The result is a continuous and holistic HMI design creating mode awareness through ambience. Findings from Phase 3 and previous research indicate that this HMI is comprehended well, with a relatively low task load, and with a good experienced system usability. It is important to additionally evaluate the HMI design resulting from the current study in driving simulators and in on-road tests. Such tests will provide an opportunity to verify and expand on the current study's findings and to contribute to guidelines for HMI design. [ABSTRACT FROM AUTHOR]

This article consists of the development of a new project carried out by a leading national company which was innovating the way to optimize its production costs and improve its processes to achieve its annual goals. This improvement began with the project to use the energy generated in its facilities. For the abovementioned company, having implemented an industrial network and a human-machine interface that allows it to acquire and quantify data on the plant's energy efficiency is the basis of its electric power generation process. Part of this electricity is fed into the public grid, so this project aims to implement a system that provides information to the control center of the electricity distribution company through a wireless communication link in order to send all the signals required by the entity responsible for energy distribution so that it can supervise and monitor them properly as stipulated by law with respect to electricity generation companies, thus ensuring compliance with all the legality of the case and finally having a successful implementation. [ABSTRACT FROM AUTHOR]

Development of a hydraulic press machine is detailed in this paper. The main aim of the device is to make standard size rock cores from powdered rock particles via hydraulic pressing after a manual preparation. In order to increase productivity, the device is equipped with three independent stations. The development process contains the mechanical, electrical design of the system and the programing of PLC and HMI modules. This paper mainly focuses on electrical design and programing of the press machine. A self-devised proportional controller module is created to control a proportional pressure relief valve. Nonlinear characteristic of the entire system is determined by measurements and considered in the PLC program. The system parameters of the machine, e.g., pressure, hysteresis range can be flexibly adjusted by user within certain limits [ABSTRACT FROM AUTHOR]

Mechanical energy harvesters including piezoelectric nanogenerators, electromagnetic generators and triboelectric nanogenerators (TENG) used to convert the mechanical motion into electricity are more and more important in the recent decades. Specifically, the fiber-based TENG (FTENG) has gained considerable favors due to its flexibility, light weight, and high environmental tolerance for the wearable devices. The traditional FTENGs made of Teflon result in better performance but are not suitable for long-term wear in person. Here, we propose a novel FTENG using a flexible micro-needle-structured polydimethylsiloxane (MN-PDMS) together with the comfortable commercially available 2D-polyester fibers, and electroless nickel-plated cotton cloth of which two are widely used in human daily life. The MN-PDMS is formed by a laser engraved mold for improving its output performance of FTENG compared to the flat-PDMS. The open-circuit voltage (Voc) and the short-circuit current (Isc) of MN-FTENG increased to 73.6 V and 36 μA, respectively, which are 34% and 37% higher than the flat-FTENG. In terms of power, the performance of MN-FTENG reaches 1.296 mW which is 89% higher than that of flat-TENG and it can also light up 90 LEDs. For application, human motion at the joints can be detected and collected with various signals that are used for the human–machine interface (HMI) through the cooperation of components for the Internet of Things (IoT). It can light up the LED bulb through MN-FTENG to potentially develop IoT HMI systems for human motion control of robot in the future. [ABSTRACT FROM AUTHOR]

The emergence of low-cost perception sensors and the abundance of inexpensive machine vision cameras, allow incorporating intelligent human user interfaces into electrical powered wheelchair platforms. Currently, these interfaces rely on computer vision and image processing algorithms. However, these algorithms require significant computing power for the autonomous navigation. A new trend to satisfy this constraint is the use of a GPU in cooperation with a CPU to accelerate computer vision and massively parallel and time-consuming applications. In the present paper, we propose a GPU-accelerated method to parallelize the well-known Viola and Jones based eyes detection algorithm. The implemented algorithm was applied to a human machine interface with a rapid feedback reaction to control a smart wheelchair designed for people with disabilities. We focused on the implementation of the system on a mobile high-performance computing CPU/GPU platform. The added value of the proposed implementation is the parallel strategies that we adopted for the Viola and Jones algorithm using OpenCL API. Compared to a multi-threaded version, for a 640 * 480 pixels image, our OpenCL C-based GPU version took around 55 milliseconds instead of 116.791 milliseconds for the multi-threaded version. By achieving nearly 2.1 speedup without precision degradation, the proposed heterogeneous strategy is useful for implementation of other computer vision methods. [ABSTRACT FROM AUTHOR]

Trust has been clearly identified as a key concept for human–machine interaction (HMI): on the one hand, users should trust artificial systems; on the other hand, devices must be able to estimate both how much other agents trust them and how trustworthy the other agents are. Indeed, the applications of trust in these scenarios are so complex that often, the interaction models consider only a part of the possible interactions and not the system in its entirety. On the contrary, in this work, we made the effort to consider the different types of interaction together, showing the advantages of this approach and the problems it allows to face. After the theoretical formalization, we introduce an agent simulation to show the functioning of the proposed model. The results of this work provide interesting insights for the evolution of HMI models.

Being the main pillar in the context of Industry 4.0, the Internet of Things (IoT) leads evolution towards a smarter and safer planet. Being human-centered, rather than machine-centered, as was the case of wireless sensor networks used in the industry for decades, the IoT may enhance human intelligence with situational awareness, early warning, and decision support tools. Focusing on air cargo transportation, the “INTELLICONT” project presented a novel solution capable of improving critical air cargo challenges such as the reduction of total aircraft weight, detection and suppression of smoke and/or fire in a container, elimination of permanent moving and locking hardware, loading and unloading logistics enhancement and maintenance. In the present work, the IoT-based monitoring and control system for intelligent aircraft cargo containers is presented from a hardware perspective. The system is based on low-cost, low-energy sensors that are integrated into the container, can track its status, and detect critical events, such as fire/smoke, impact, and accidental misuse. The focus has been given to the design and development of a system capable of providing better and safer control of the aircraft cargo during the loading/unloading operations and the flight. It is shown that the system could provide a breakthrough in the state of the art of current cargo container technology and aircraft cargo operations.