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Recent research has extensively examined in-vehicle display designs for supporting the operation of driving automation. As automation relieves drivers from various driving tasks including vehicle control (e.g. steering, accelerating, and braking), driving performance measures (e.g. speed, lane deviations) may not be informative indicators for evaluating the effectiveness of in-vehicle displays. Gaze-based measures are a better alternative given their link to driver visual attention, an indication of driver engagement. A scoping review was conducted to review the literature on the design of displays for supporting the operation of driving automation and the evaluation of these displays using gaze-based measures. Forty-three articles were included in the review. Most of the studies investigated visual (and mixed visual-auditory) displays that provide alerts to the driver for when to intervene automation classified as Level 3. The adopted gaze measures mostly relied on static areas of interest (AOIs), with fewer studies looking at more fine-grained, context dependent AOIs. The paper summarises the findings of the review, including research trends and gaps, as well as recommendations for future research. [ABSTRACT FROM AUTHOR]

This paper proposes and demonstrates a simulator-based approach for testing and assessing human operators' ability and performance in supervising autonomous ships. In the autonomy concept studied here, it is assumed that the autonomous navigation system is capable of detecting and notifying the human operator prior to entering a challenging situation. The system will attempt to resolve the situation with a proposed evasive maneuver, but the system may occasionally make errors or select sub-optimal solutions. When the human operator is notified about a challenging situation, the operator should closely assess the situation, and intervene if (and only if) necessary. The proposed approach allows us to test and quantify the human operators' abilities and performances in supervising an autonomously navigated ship. The approach is demonstrated on 56 simulator experiments, involving seven different navigators that perform eight different traffic scenarios. The scenarios are all based on real traffic data collected from a Norwegian ferry-crossing. The demonstration shows that the candidates are successful in supervising the autonomous ship in low-complexity traffic scenarios where it is easy for the operator to interpret the system's decisions. The operators' ability to intervene when and only when needed does, however, decline in more complex scenarios, and their performance is highly dependent on the traffic scenario and vary significantly between the candidates. [ABSTRACT FROM AUTHOR]

This research introduces IVIPAT: an in-vehicle information processing analysis tool. The tool consists of a taxonomy (e.g., filter, search, interpret, monitor, decide) that can be used to carry out a task analysis with focus on information processing. This taxonomy is combined with a subjective mental demand rating scale to identify the intensity of each information processing step. The taxonomy is evaluated and revised based on the feedback of N = 15 practitioners in Study 1 and then applied by a second sample of N = 15 practitioners in Study 2. Results illustrate the added value of the tool: through the rating, user interaction flow variants with differences regarding the mental demand can be identified. In this case, text input via rotary knob was rated as significantly more demanding than text input via handwriting (p <.01, df = 14) or keyboard (p <.05, df = 14). When analyzing information processing during the three user interaction flows, the analysts assigned a total of 359 mental operators with the help of the taxonomy. Results show that the increased load while using the rotary knob could be explained by the necessary interpretation of the direction of rotation, the tracking of the visual highlight during rotation and the overall increased number of information processing steps during interaction. Overall, the focus on information processing and applicability for practitioners distinguish the presented approach from previous ones, like keystroke-level models and cognitive architectures. To facilitate the application, survey materials and analysis examples are included. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• A framework for studying the intersection-approaching behavior is proposed and the human-machine interaction is visualized. • A signal energy detection method based on the wavelet transform is then developed to monitor the driver's response behavior. • Finally, a Bayesian non-parametric method is used to quantify the distribution of driving patterns. The in-vehicle communication provides promising opportunities to improve the road safety and traffic efficiency. Previous studies demonstrated that the professional drivers have better driving skills than the non-professional drivers who allocate more attention to secondary tasks. However, they may not be sensitive to the new in-vehicle technology. In addition, these qualitative studies failed to elaborate on the visual and response behavior differences among different driver groups (professional drivers such as taxi, bus, motorcoach, and non-professional drivers), and lacked the quantitative analysis of driving patterns in a new environment. This paper explores the differences in visual interaction, response characteristics, driving performance, and behavior patterns between the professional and non-professional drivers in the connected environment through a case study of intersection-approaching behavior using a driving simulator. More precisely, two driving scenarios (baseline and human–machine interface (HMI)) were designed in the driving simulator, and 65 participants, including 34 professional drivers and 31 non-professional drivers, completed the experiment. In the HMI scenario, the driver was provided with the signal light phase and phase transition remaining time of the current intersection. This paper also proposes a driving pattern extraction model based on the Bayesian non-parametric method combined with a text clustering algorithm to perform a quantitative description of the driving patterns. The results show that the professional drivers tend to interact less with the HMI compared with the non-professional drivers. Moreover, the professional drivers' first gaze at the HMI occurs and responds earlier. The proposed driving model can effectively describe 7 patterns of intersection-approaching behavior. The connected information can significantly improve the efficiency of the intersection traffic and the driving behavior. However, the professional drivers are more responsive and behave more consistently. This study can provide insights into the development of personalized assisted driving systems, as the two driving populations differ in their interactions, responses, and behavioral patterns. [ABSTRACT FROM AUTHOR]

Copyright of Techno.com is the property of Universitas Dian Nuswantoro, Fakultas Ilmu Komputer and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Copyright of FME Transactions is the property of University of Belgrade, Faculty of Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Residential buildings can contribute to save energy and to decrement electricity consumption in the world. On the other hand, the Internet of Things has allowed the implementation of smart homes that can profile the users. Nevertheless, end-users are not accepting the smart homes due to behavioral problems and usability problems with the Human–Machine Interface (HMI) or with the household appliances. As a solution, social products promote the interaction between the smart home and the consumer by including gamification features in the interface. Thus, smart homes can interact and compete with other houses to reduce energy consumption. Therefore, this paper proposes a three-step framework that takes advantage of social products to promote interaction between smart homes within a smart community to reduce electrical energy consumption. The first step collects from the literature review, the characteristics of the end-users, the behavioral and usability problems, and the most common gamification elements that teach, engage and motivate the user to reduce energy consumption. The second step proposes the gamification elements required for a tailored HMI in each social product and smart home through a fuzzy logic decision. The third step evaluates the interaction between social products in smart homes and the users to test which smart home is reducing more energy consumption. Finally, a three-level tailored gamified mock-up is depicted: level 1 for a single social product, level 2 for the smart home, and level three for the smart community. This mock-up can be implemented in small communities as residential complexes.

In the recent industry field, automation has become the most important role in production processes to improve efficiency, increase production, and reduce workers. Sorting the products based on their color is a common task in the industries, but using manual sorting may take a longer time and human error may occur, especially if the production quantities are very large. To contribute to improving the quality and speed of production, we have proposed a new system to automatically separate products based on their color. This article presents a new system for identifying and separating industrial products based on their colors. The implemented system consists of three sensors used to detect the colors of the products, four chutes, and two conveyor belts designed to guide objects along the manufacturing path, and three push mechanisms to place objects in their designated places. The system includes an emergency interface, a reset interface, a stop interface, as well as communication using the PN (PROFINET Process Field Net) protocol, a programmable logic controller, and a 1511-1 central processing unit. This system has been precisely programmed and simulated using FACTORY I/O software and TIA PORTAL software to control and monitor the system effectively. The Supervisory Control and Data Acquisition system also includes a Human–Machine Interface user interface for maximum control. The performance of this system was tested by sorting 250 objects of different colors. These tests resulted in the successful separation of a total of 85 blue objects, 89 green objects, and 86 gray (metallic) objects within a time period of 20 min. This system shows wide applicability in various industries including diamond mining, recycling, and grain manufacturing sectors.

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. [ABSTRACT FROM AUTHOR]

With artificial intelligence (AI) and automation embedded in most aspects of life from healthcare to transportation, this article conceptualizes how increasing automation will benefit or inhibit the therapeutic encounter. Five levels of a model are outlined, from the human analogue to the fully autonomous virtual (AI) therapist. The levels are referenced against key elements of the therapeutic encounter known to contribute to an effective therapeutic outcome, from the working alliance, to collaborative elements, to a confidential setting. Losses and gains are discussed through these and other aspects, such as the neurobiological, and the capability to notice what is not there in the process as well as what is, around the client's aim of change or awareness. The objective is not to pit human therapists against non-human systems, but to propose a human-centred framework for the integration of AI into clinical reality, by introducing a taxonomy model of human–machine interaction (HMI), to see how each contributes, and to evaluate areas of risk and harm. Questions are raised around ethics, therapeutic process and crucially the very nature of what it is to be in a therapeutic encounter for a human client together with a human or AI therapist. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This research paper examines the evolution of human-machine interfaces (HMIs) in the manufacturing and logistics sectors in the context of Industry 5.0 (I5.0). I5.0 introduces a human-centered paradigm, and as digitalization transforms processes in these domains, human-machine interaction is undergoing a notable shift. Yet, there is a lack of I5.0 HMI requirements and design guidelines. Through a literature review, the research identifies and investigates general requirements for HMIs in I5.0. Findings include ethical principles, ergonomics, adaptability, human-centered design, intuitive interaction, and effective collaboration. It also uses a case study approach to highlight potential strategies and discuss challenges. The results provide valuable insights into considerations for designing effective HMIs for I5.0 applications, enhancing the user experience, improving productivity, and facilitating successful human-machine interactions. [ABSTRACT FROM AUTHOR]

Proses penalaan tuning PID seringkali dilakukan dengan cara uji coba akibatnya hasil tuning tidak akurat dan proses penalaannya memakan waktu yang cukup lama. Penelitian ini bertujuan untuk menerapkan metode Zieghler-Nichols I atau metode kurva reaksi yang mampu menghasilkan nilai tuning dengan praktis serta nilai hasil parameter tuning yang baik. Pada penelitian ini menggunakan PLC Siemens S7-300 sebagai pusat kontrol dan HMI Siemens TP-700 Comfort sebagai interface sistem serta menggunakan MS150 Modular Servo sebagai media kendali PID. Penelitian ini dilakukan dengan dua cara. Pertama, pengujian pembacaan tegangan pada sensor tachogenerator. Kedua, pengujian respon sistem dari kendali PID. Pada pengujian pertama didapatkan hasil dengan nilai error tertinggi pada nilai 0.029%. Pengujian kedua didapatkan performa dari respon sistem Kp=1.125, Ti=2.667, dan Td=0 dengan hasil terbaik pada setpoint 3000 rpm, waktu rise time 3 detik, delay time 1 detik, settling time 7 detik serta tanpa adanya overshoot pada sistem. Dapat disimpulkan bahwa nilai tuning dengan menggunakan metode Zieghler-Nichols I lebih praktis dan menghasilkan respon sistem cukup stabil dengan performa yang baik.

Pengukuran dan pengendalian ketinggian cairan masih dilakukan secara direct, menggunakan saklar atau sensor yang bersifat logic saja. Dengan konsep kontrol ini membutuhkan lebih banyak waktu, dan tidak secara real-time. Penelitian ini bertujuan untuk membuat dan merancang sistem kendali level cairan pada tangki menggunakan PLC Siemens S7-1200 sebagai pusat kontrol dan HMI KTP 700 BASIC sebagai pusat kendali dan monitoring level cairan pada tangki. Metode perancangan system yang digunakan dalam penelitian terdiri dari perancangan hardware dan software. Perancangan hardware terdiri dari PLC Siemens S7-1200 sebagai pusat kontrol, water pump sebagai penyuply cairan dan potensiometer difungsikan sebagai sensor yang akan membaca ketinggian cairan serta HMI Siemens KTP 700 Basic digunakan untuk interface pembacaan hasil ketinggian secara digital. Driver motor dengan konsep PWM (Pulse Width Modulation) digunakan untuk mengatur kecepatan pompa dengan memvariasikan duty cycle motor yang dimanfaatkan untuk mengisi air kedalam tangki. Serial komunikasi antara PC, PLC dan HMI menggunakan ethernet switch. Pengujian dilakukan dengan lima cara, pertama pengujian potensiometer, kedua pengujian akurasi tegangan potensiometer terhadap ketinggian level air, ketiga pengujian PWM, keempat pengujian sistem tanpa gangguan, dan kelima pengujian sistem dengan gangguan. Dari pengujian pertama didapatkan hasil bahwa nilai rata-rata dari error sensor sebesar 0.005714 %. Dengan demikian penelitian ini mendapatkan hasil alat sistem kendali dan monitoring level cairan berbasis PLC dan HMI dengan error rata-rata sebesar 9.5 detik serta memiliki kemampuan terbaik untuk pompa pada set point 70 %.

The shared responsibility between conditional AVs drivers demands shared understanding. Thus, a shared intended pathway (SIP)—a graphical display of the AV's planned manoeuvres in a head-up display to help drivers anticipate silent failures is proposed. An online, randomised photo experiment was conducted with 394 drivers in Australia. The photos presented traffic scenarios where the SIP forecast either safe or unsafe manoeuvres (silent failures). Participants were required to respond by selecting whether driver intervention was necessary or not. Additionally, the effects of presented object recognition bounding boxes which indicated whether a road user was recognised or not were also tested in the experiment. The SIP led to correct intervention choices 87% of the time, and to calibrating self-reported trust, perceived ease of use and usefulness. The bounding boxes found no significant effects. Results suggest SIPs can assist in monitoring conditional automation. Future research in simulator studies is recommended. Practitioner summary: Conditional AV drivers are expected to take-over control during failures. However, drivers are not informed about the AV's planned manoeuvres. A visual display that presents the shared intended pathway is proposed to help drivers mitigate silent failures. This online photo experiment found the display helped anticipate failures with 87% accuracy. [ABSTRACT FROM AUTHOR]

Currently, there are limitations in the human–machine interfaces (HMIs) used in industry, either due to the characteristics of users' cognitive abilities or interfaces, which hinder communication and interaction between humans and equipment. For this reason, this work presents an alternative interaction model based on a voice assistant, Alexa, which promotes more natural, intuitive, direct, and understandable communication. The purpose of this work is the development of an industrial safety system for a controlled electric motor based on Alexa voice assistant, which allows the monitoring of its operating parameters, such as phase current, housing temperature, and rotor vibration, as well as making it possible to control ignition and shut down and change the rotation of the motor with a prior password, as a safety measure. Commercial smart devices and Arduino-compatible modules were used to achieve this, providing them with the Internet of Things (IoT) feature. In addition, several software platforms, such as Blynk, Tuya Smart, Node Red, and Voiceflow, are used to perform data transmission, device management, and programming of the Alexa skill, oriented to the execution of the security and run system. This shows the potential capacity of voice assistants in the industry to deliver information more naturally to humans and obtain optimal notifications. However, problems were evidenced, such as the influence of noise in the environment when communicating with the assistant, the vocalization of words, low voice tones, and accents typical of the language, that will increase the security level of the system and prevent potential identity theft. [ABSTRACT FROM AUTHOR]

Motion Sickness (MS) remains a paramount concern in the evolving landscape of autonomous vehicles. As automation continues to reshape the passenger-vehicle interaction paradigm, the proliferation of diverse in-vehicle systems empowers passengers to disengage from the driving task. However, this new paradigm comes with the potential cost of exacerbating MS inside the car.In this work, we intended to: (1) present and review available countermeasures to prevent or mitigate MS, found in the literature, that could also be implemented inside a car; (2) identify current trends and gaps in countermeasures to MS; and (3) suggest future avenues of research for potential use-cases aiming to mitigate MS in autonomous driving.Through a comprehensive review of 65 publications, spanning from 2009 to 2023, we have organized existing literature into three distinctive categories and ten subcategories: (1) Vehicle-Centric Adaptation (Adaptation to Surface, Best Route ad Driving Style Adaptation), (2) In-Car Design and Environment (Design Guidelines for Seats, for Displays, for Windows and Climate Control), and (3) Sensory Cues (Visual, Haptic Audio and Olfactory Cues).Our findings suggest that Visual Cues and Motion Planning are the two strongest trends in MS countermeasures. In contrast, Olfactory Cues are the least studied approach. Our results also substantiate the viability of multimodal approaches as a promising solution for passengers in autonomous vehicles. The simultaneous application of various countermeasures might hold potential in mitigating MS effectively.As autonomous vehicles advance, these findings offer a strong basis for future research to decrease passenger's motion sickness and improve their well-being, safety, and comfort inside the car.

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.

This study aims to facilitate the process of sorting objects made of metal and non-metal so that they can be sorted based on these criteria to the place provided based on a Programmable Logic Controller using the Human Machine Interface as the control center of the tool. Where the Human Machine Interface can be used as a controller and monitor the sorting of objects which is done with two automatic and manual selection methods. Inappropriate program design and use of conventional actuators. The pneumatic system acts as an actuator for sorting goods, namely a double acting cylinder which is installed to push metal and non metal goods towards the conveyor based on what is detected by the capacitive proximity sensor, the cylinder which is installed on the inductive proximity sensor is used to sort metal objects towards storage. If the object is metal, the sensor will detect it and the conveyor will stop and the cylinder will sort the metal object to the metal storage. Non-metal objects will be passed by the sensor to non-metal storage.

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.

In order to improve the degree of baking automation of dryer, and realize the feeding and hot processing of sunflower seeds, a control system for double layer sunflower seeds dryer with PLC as the core processor and touch screen as the man-machine interface was designed. The control of the double layer sunflower seeds dryer was realized by connection with the actuating and sensing components in combination with electric design. The system can realize the functions such as setting and adjustment of process parameters, monitoring of running state, recording of historical data. The average score of subjective evaluation of quality and efficiency is 43.74 points, which is much higher than 38.26 points of the single-layer oven. The experiment shows that the system is simple and its running is stable, and the subjective score is improved by 11% compared with the comparison equipment. It is suitable for the control of double layer sunflower seeds dryer. The research provides reference for ensuring high-quality sunflower seeds baking process. [ABSTRACT FROM AUTHOR]

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]