Intelligent Mobile Research Articles

Tourism Recommendation Algorithm Based on the Mobile Intelligent Connected Vehicle Service Platform

As to the problems in current tourism recommendation, this paper proposes a tourism recommendation algorithm based on the mobile ICV service platform. Firstly, the ICV service system for the Point of Interest (POI) searching and route recommendation is designed. Secondly, the recommendation service model is set up from two aspects, namely the tourism POI clustering algorithm and the tourism POI searching and route recommendation algorithm. In the aspect of symmetrical-based matching features, the clustered POIs are matched with the tourists’ interests, and the POIs in the neighborhood of the ICV dynamic locations are searched. Then, a POI recommendation algorithm based on the tourists’ interests is constructed, and the POIs that best match the symmetrical interests of the tourists within the dynamic buffer zones of ICV are confirmed. Based on the recommended POIs, the ICV guidance route algorithm is constructed. The experiment verifies the advantages of the proposed algorithm on the aspect of the POI matching tourists’ interests, algorithm stability, traveling time cost, traveling distance cost and computational complexity. As to the iterative sum and the iterative sum average of the POI matching function values, the proposed algorithm has a performance improvement of at least 20.2% and a stability improvement of at least 20.5% compared to the randomly selected POIs in matching tourists’ interests. As to the cost of the guidance routes, the proposed algorithm reduces the average cost by 19.6% compared to the other suboptimal routes. Compared with the control group algorithms, the proposed algorithm is superior in terms of route cost, with an average cost reduction of 13.8% for the output routes compared to the control group. Also, the proposed algorithm is superior in terms of route cost compared to the control group recommendation algorithms, with an average cost reduction of 11.2%.

Thermal calibration for triaxial gyroscope of MEMS-IMU based on segmented systematic method

With the progress of micro electromechanical system (MEMS) technology, the performance of MEMS inertial measurement unit (IMU) composed of gyroscopes and accelerometers has been improved. Among the inertial sensors, MEMS triaxial gyroscope plays an important role in attitude estimation, navigation and positioning of intelligent mobile terminals such as unmanned aerial vehicles and unmanned vehicles. However, the measured values of low and medium cost MEMS triaxial gyroscopes are mainly affected by temperature (or thermal effect) and random errors. As results, the drift errors correlated with temperature will reduce application accuracy of MEMS triaxial gyroscope. The traditional calibration method for thermal drift errors relies on the expensive equipment, such as turntable and the temperature control system, which increases the cost of calibration. Therefore, an effective thermal calibration method that is available when using low- or high-cost tools for MEMS triaxial gyroscope will be meaningful for majority users. Hence, this paper analyzed and established the thermal drift model of MEMS triaxial gyroscope, and proposed a segmented systematic calibration method based on 24 states according to this model. Simulation shows that the proposed method can obtain the results approaching the real parameter thermal drift curves. Calibration experiments and parameters test show that the max errors of attitude calculation are reduced to 10% of the results using the original parameters, which indicates that the effectiveness of the proposed method.

Research on Path Planning for Intelligent Mobile Robots Based on Improved A* Algorithm

Research on Path Planning for Intelligent Mobile Robots Based on Improved A* Algorithm

An effective farmer-centred mobile intelligence solution using lightweight deep learning for integrated wheat pest management

Integrated Pest Management (IPM) techniques have been widely used in agriculture to manage pest damage in the most economical way and to minimise harm to people, property and the environment. However, current research and products on the market cannot consolidate this process. Most existing solutions either require experts to visually identify pests or cannot automatically assess pest levels and make decisions based on detection results. To make the process from pest identification to pest management decision making more automated and intelligent, we propose an end-to-end integrated pest management solution that uses deep learning for semi-automated pest detection and an expert system for pest management decision making. Specifically, a low computational cost sampling point generation algorithm is proposed to enable mobile devices to generate uniformly distributed sampling points in irregularly shaped fields. We build a pest detection model based on YoloX and use Pytorch Mobile to deploy it on mobile phones, allowing users to detect pests offline. We develop a standardised sampling specification and a mobile application to guide users to take photos that allow pest population density to be calculated. A rule-based expert system is established to derive pest management thresholds from prior agricultural knowledge and make decisions based on pest detection results. We also propose a human-in-the-loop algorithm to continuously track and update the validity of the thresholds in the expert system. The mean average precision of the pest detection model is 58.17% for 97 classes, 75.29% for 2 classes, and 57.33% for 11 classes on three pest datasets, respectively. The usability of the pest management system is assessed by the User Experience Surveys and achieves a System Usability Scale (SUS) score of 76. The usability of the proposed solution is validated by qualitative field experiments.

Human resources management in the age of artificial intelligence

Today's businesses are operating in a complex environment, marked by the emergence of a digital culture that is transforming space and time, as well as relationships at work. These various transformations have given rise to a new concept: artificial intelligence. With the introduction of artificial intelligence, work is being transformed, creating new challenges for organizations and leading to new HR practices in terms of recruitment, training, compensation, talent management and so on.For all these reasons, the mobilization of collective intelligence is ultimately becoming a priority for HR, insofar as it makes it possible to support transformation within organizations by means of agile methods. Against this backdrop of changing organizational practices, a number of questions arise:-What is artificial intelligence and how does it impact organizations? -What role can collective intelligence and artificial intelligence play in the evolution of the HR function?The aim of this theoretical research is to underpin the various concepts linked to these notions. We will also try to understand what is at stake in collective intelligence for HR, and how artificial intelligence can impact organizational practices.

Envisioning the Future of Mobility: A Well-Being-Oriented Approach

Mobility, a vital part of daily life, significantly impacts human well-being. Understanding this relationship is crucial for shaping the future trajectory of mobility, a connection often overlooked in previous research. This study explores the complex relationship between mobility and well-being and proposes a holistic framework for mobility’s future, prioritizing individual and societal well-being. The motivation for this research stems from the growing need to balance technological advancements in transportation with the well-being of diverse populations, especially as the mobility landscape evolves with innovations like autonomous vehicles and intelligent mobility solutions. We employ bibliometric methods, analyzing 53,588 academic articles to identify key themes and research trends related to mobility and well-being. This study categorizes these articles into thematic clusters using the Louvain modularity maximization algorithm, which facilitates the formation of cohesive groups based on citation patterns. Our findings underline the significant impact of mobility on physical, mental, psychological, financial, and social well-being. The proposed framework features four pillars: vehicle, infrastructure and environment, mobility stakeholders, and policy. This framework underscores the importance of collaboration between institutional and individual actions in shaping a future mobility landscape that is technologically advanced, socially responsible, and conducive to an improved quality of life.

Tunable Lens Driven by Electrohydrodynamic Pumping

Developing soft actuators has emerged as one of the most promising areas in robotics, with a growing demand for tunable lenses driven by the rapid advancements in intelligent mobile devices and cameras. Traditional mechanical lens systems are characterized by their heaviness, complexity, and rigidity. To address these limitations, this research introduces a novel approach: a dual‐chamber liquid lens actuated by an electrohydrodynamic (EHD) pump. The study involves the fabrication of a planar EHD pump, with an experimental investigation into the impact of electrode gap, electrode width, and electrode‐pair gap on the pump's pressure. Following the optimization of the EHD pump, a dual‐chamber lens is constructed, comprising two chambers, a rigid transparent plate, and two prestretched elastic membranes, which are connected to the EHD pump via tubes. Additionally, a mathematical model is developed to predict the focal length of the lens. Both experimental and theoretical analyses are conducted to explore the effects of the prestretch ratio of the elastic membranes and the radius ratio of the two chambers on focal length variation. The theoretical predictions of the focal length–voltage curves align closely with the experimental findings, providing validation for the proposed theoretical model.

An agent-based simulation modeling framework for Mobility-as-a-Service (MaaS)

This study develops an agent-based intelligent Mobility-as-a-Service (MaaS) simulation model consisting of three types of agents (i.e., MaaS fleet unit, travelers, and central intelligent mobility assignment module) to assess mobility service assignment processes balancing conflicting entities (e.g., demand and supply) within the MaaS ecosystem. The study follows a two-level optimization method (i.e., lower, and upper levels). It employs artificial intelligence and multicriteria decision making to solve two-dimensional mobility assignment problems (demand vs. supply). The novelty of this assignment process is that it implements an intelligence module accounting for the past system performance to make a future assignment decision in favor of both sides of the operation. This study processes 24-hour trip requests extracted from Nova Scotia Travel Activity (NovaTRAC) survey data in real-time. Two scenarios (1-D and 2-D assignments) are compared using the cost criteria, such as total waiting time, empty time, and idle time. The 1-D scenario refers to mobility assignment that emphasizes the demand side only, and the 2-D scenario formulates mobility assignment by balancing the demand and supply sides of the MaaS ecosystem. Experimental results indicate that a MaaS fleet of 350 units is the most balanced fleet in both scenarios. However, the 2-D optimization method reduces the overall supply cost by 25%. Moreover, 2-D operation demonstrates a higher fleet utilization over a considerable period, whereas the 1-D design guarantees a higher fleet utilization only during the peak period. Results of this study provide us with a benchmark for assessing more complex MaaS operation scenarios which will further aid in advancing the operational MaaS ecosystem. Our findings can help policymakers implement cost-effective MaaS solutions supporting sustainable urban mobility and SDG 13: Climate Action.

Cognitive Navigation for Intelligent Mobile Robots: A Learning-Based Approach with Topological Memory Configuration

Cognitive Navigation for Intelligent Mobile Robots: A Learning-Based Approach with Topological Memory Configuration

Sustainable Transportation Solutions for Intelligent Mobility: A Focus on Renewable Energy and Technological Advancements for Electric Vehicles (EVs) and Flying Cars

(1) Introduction: Transportation logistics play a pivotal role in facilitating both individual mobility and supply chain operations on a global scale. However, conventional transportation systems have contributed significantly to urban congestion and environmental degradation. In response to these challenges, there is growing momentum to investigate the potential of renewable energy to power electric vehicles (EVs) and flying automobiles, aiming to reduce fossil fuel dependence and carbon emissions. (2) Objectives: By analyzing key drivers and opportunities for integrating flying vehicles into existing infrastructure through dynamic modeling, this study seeks to accelerate the transition to sustainable transportation. (3) Methodology: A mixed-method approach, combining literature review and causal loop diagram analysis, is employed to understand the factors influencing EV and flying car adoption. (4) Results: Findings highlight the significant potential of renewable energy in reducing emissions and reliance on fossil fuels through widespread EV and flying vehicle adoption. The major drivers and challenges in infrastructure, safety, and airspace management are also identified. In addition, this research emphasizes the importance of sustainable transportation in addressing urban congestion, pollution, and energy security. (5) Conclusions: By leveraging renewable energy and embracing technological advancements, a low-carbon future for transportation can be achieved, benefiting both the environment and society.

Intermediaries (not) in action: Impediments to initiating the utilization of public research

This paper addresses a critical research gap in the field of university-industry knowledge transfer, with a particular focus on the often-neglected initiation stage from a failure analysis perspective. A workshop and follow-up interviews were conducted to systematically explore the motivations and abilities of both the knowledge senders and the recipients for initiating knowledge transfer processes in the field of intelligent mobility. In contrast to conventional approaches, our study does not presuppose successful knowledge transfer initiation; instead, it offers a unique opportunity to investigate the phenomenon of its omission. The intricate interplay among actors and their differing perceptions of research applicability, which leads to specific tasks for intermediaries in initiating knowledge transfer processes, is analyzed. The findings of this study indicate that both internal and external intermediaries encounter challenges in fulfilling their roles, particularly in initiating the utilization of public research. The key challenges identified include a flawed understanding of scientific research, a lack of initiative spirit, and a disregard for actual responsibilities. This study offers valuable insights that can inform the development of evidence-based policies aimed at initiating the utilization of public research.

Justin Chan: Intelligent Mobile Systems for Equitable Healthcare

Justin Chan: Intelligent Mobile Systems for Equitable Healthcare

Optimizing ORB-SLAM For Varied Weather Conditions Using Genetic Algorithm

Abstract In the rapidly evolving domains of self-driving cars, the resilience of Simultaneous Localization and Mapping (SLAM) algorithms to varying environmental conditions remains a critical challenge. This paper leverages the CARLA simulator to create comprehensive datasets that encompass an array of weather scenarios, ranging from clear sky to complex combinations of fog and rain, during both daytime and nighttime. The primary objective of this study is to optimize the performance of ORB-SLAM2 under these harsh conditions, improving resilience and robustness against different weather conditions. The evaluation is conducted using the Root Mean Square Error (RMSE) as the key metric. Genetic Algorithm (GA) is developed to optimize the parameters of ORB-SLAM. The GA aims to reduce the RMSE for each unique weather situation. The results show a significant improvement in ORB-SLAM’s performance and resilience, contributing to its potential applications in the broader landscape of autonomous systems and intelligent mobility networks.

Let’s walk! The fallacy of urban first- and last-mile public transport

AbstractIn recent years, there has been an upsurge in intelligent mobility solutions that provide door-to-door services. Although these services offer convenience to certain individuals, it is frequently overlooked that they can lead to welfare losses when accounting for the reduced health benefits that result from reduced physical activity. In this paper, we derive a welfare function of introducing first- and last-mile public transport services. By comparing possible health gains from walking with corresponding accessibility losses, we identify the distance boundaries under which the service fails to be socially beneficial. The results are based on a simulation study and draw on further insights from a recent agent-based model from Copenhagen focusing on first- and last-mile public transport. Although the model is intentionally stylized and may not apply universally to all scenarios featuring diverse population densities, demographic profiles, or transport network layouts, the fundamental conclusion presented in the paper is that first-mile services have minimal welfare impact for average trip distances below 1 km, appears robust even under conservative assumptions. In this case, the probability of failure is almost 100% for any realistic parametrization. This finding implies that planners and researchers should focus on the design of main transit networks and the access and egress of active modes to and from the stations. In particular, door-to-door services covering shorter distances should not be the priority of public funding unless in particular situations or contexts.

Effects of the "AI-TA" Mobile App With Intelligent Design on Psychological and Related Symptoms of Young Survivors of Breast Cancer: Randomized Controlled Trial.

Young women often face substantial psychological challenges in the initial years following cancer diagnosis, leading to a comparatively lower quality of life than older survivors. While mobile apps have emerged as potential interventions, their effectiveness remains inconclusive due to the diversity in intervention types and variation in follow-up periods. Furthermore, there is a particular dearth of evidence regarding the efficacy of these apps' intelligent features in addressing psychological distress with these apps. This study aims to evaluate the effectiveness of a mobile app with intelligent design called "AI-TA" on cancer-related psychological health and ongoing symptoms with a randomized controlled design. Women aged 18 to 45 years diagnosed with breast cancer were randomly assigned to the intervention or control group. The intervention was AI-TA, which included 2-way web-based follow-up every 2 weeks. Both intention-to-treat (ITT) and per-protocol (PP) analyses employed repeated measurement analysis of variance. The participants' background features, primary outcomes (psychological distress and frequency, self-efficacy, and social support), and secondary outcomes (quality of life) were measured using multiple instruments at 3 time points (baseline, 1-month intervention, and 3-month intervention). A total of 124 participants were randomly allocated to the control group (n=62, 50%) or intervention group (n=62, 50%). In total, 92.7% (115/124) of the participants completed the intervention. Significant improvements in psychological symptoms (Memorial Symptom Assessment Scale-Short Form) were observed in the ITT group from baseline to 1-month intervention relative to the control group (ITT vs control: 1.17 vs 1.23; P<.001), which persisted at 3-month follow-up (ITT vs control: 0.68 vs 0.91; P<.001). Both the ITT and PP groups exhibited greater improvements in self-efficacy (Cancer Behavior Inventory-Brief Version) than the control group at 1-month (ITT vs PP vs control: 82.83 vs 77.12 vs 65.35; P<.001) and 3-month intervention (ITT vs PP vs control: 92.83 vs 89.30 vs 85.65; P<.001). However, the change in social support (Social Support Rating Scale) did not increase significantly until 3-month intervention (ITT vs control: 50.09 vs 45.10; P=.002) (PP vs control: 49.78 vs 45.10; P<.001). All groups also experienced beneficial effects on quality of life (Functional Assessment of Cancer Therapy-Breast), which persisted at 3-month follow-up (P<.001). The intelligent mobile app AI-TA incorporating intelligent design shows promise for reducing psychological and cancer-related symptoms among young survivors of breast cancer. Chinese Clinical Trial Registry ChiCTR2200058823; https://www.chictr.org.cn/showproj.html?proj=151195.

A Dynamic Task Offloading Scheme Based on Location Forecasting for Mobile Intelligent Vehicles

A Dynamic Task Offloading Scheme Based on Location Forecasting for Mobile Intelligent Vehicles

Research on the Expansion Application of Electronic Toll Collection System

Electronic Toll Collection (ETC) system is an important part of modern intelligent transport system, which plays an important role in intelligent car parks and congestion charging. Electronic Toll Collection (ETC) system includes automatic vehicle identification system and electronic billing system. The automatic vehicle identification system includes three parts: automatic vehicle identification (AVI), automatic vehicle model identification (AVC) and inspection system (ES).The application of ETC in intelligent car parks and congestion charging also includes automatic vehicle identification system (automatic vehicle identification, automatic vehicle model identification and inspection system) and electronic billing system. In intelligent car parks, ETC technology achieves fast and accurate automatic toll payment by automatically identifying the ETC tags carried by vehicles, which greatly improves the efficiency of car parks and reduces the queuing and waiting time due to manual toll collection. In terms of congestion charging, ETC technology provides an effective means to implement congestion charge collection. By setting up ETC toll lanes in core urban areas or busy road sections, the system can automatically record the information of the vehicles passing through and carry out toll settlement, thus realising dynamic toll collection according to the degree of congestion and the frequency of vehicle use. This congestion charging strategy aims to regulate urban traffic flow through economic means, alleviate traffic congestion and promote the sustainable development of urban transport. Although China's theoretical research on congestion charging, has been relatively mature, but affected by a variety of factors, no city has yet to start the implementation of congestion charging, the lack of experience in practice. Singapore will not stop the car charging system (ETC) technology successfully applied to urban congestion charging and intelligent car parks, ETC system in intelligent car parks and congestion charging application, help to promote the development of urban transport to a more intelligent, green direction. China can learn from the successful experience of foreign countries and apply it to its own intelligent car parks and congestion charging. This paper introduces the basic principle of ETC, discusses the research progress of ETC in terms of technical characteristics, and analyses the application scenarios of ETC in intelligent car parks and congestion charging. By examining the opportunities offered by Electronic Toll Collection (ETC) systems in smart car parks and congestion pricing, this study aims to delve into the transformative impacts of Electronic Toll Collection (ETC) systems on intelligent mobility and contribute to the sustainable development of intelligent mobility.

Autonomous and Intelligent Mobile Multimedia Cyber-Physical System with Secured Heterogeneous IoT Network

Autonomous and Intelligent Mobile Multimedia Cyber-Physical System with Secured Heterogeneous IoT Network

Energy-efficient mobile edge computing assisted by layered UAVs based on convex optimization

The utilization of unmanned aerial vehicles (UAVs) as aerial mobile edge computing (MEC) servers presents an effective approach for mitigating the computational limitations of intelligent mobile devices. However, the limited battery capacity of UAVs poses a significant challenge in ensuring sustained service provision. To counter this obstacle and enhance energy efficiency, we introduce a partial offloading MEC scheme assisted by layered UAVs (POAL). Our objective is to minimize the total energy consumption of UAVs by jointly optimizing several factors, including wireless channels, transmit power, task partition, computing power, and UAV trajectories. Due to the non-convexity of the problem, we decompose it into three sub-problems and address them iteratively. Firstly, we employ the CVX optimization tool to allocate wireless channels and utilize Lagrangian duality to determine the optimal transmit power. Secondly, leveraging the monotonicity of the optimization objective, we derive the optimal computing power. Lastly, we employ the successive convex approximation (SCA) technique to tackle the trajectory planning sub-problem. Numerical results demonstrate that the proposed algorithm can effectively position UAVs within 15 iterations. Furthermore, our layered scheme significantly reduces overall energy consumption.

Inclusiveness of Public Space: Experimental Approaches for the Revitalisation of Smaller Historic Urban Centres

In recent decades, many small Italian historic centers—particularly those situated inland—have witnessed a population decline due to inadequate access to public services and facilities. However, this depopulation has also allowed many centers to retain their distinctive features, now conferring upon them great cultural–historical and landscape value. New quality-of-life-centered economic models present the development of accessible public services as a necessity. Such a process could catalyze the recovery and growth of these centers, which continue to be deserted, regardless of their value. This paper considers combined solutions, including sustainable mobility, digital accessibility, networked services, and technological devices by applying them to trans-scalar studies with the goal of achieving sustainable outcomes. Some of the proposed solutions are the resolution of irregular ground levels, the use of electric vehicles, the creation of sharing models, the physical overhaul of routes, and the retrofitting of minor buildings for inclusive use in a comprehensive human-centered approach toward regeneration. This study is in line with the European guidelines for sustainable and intelligent mobility, whose goal is for at least one hundred European cities to become accessible to all and shifted to zero-emission mobility. Here, sustainable and smart mobility is understood not only as an improvement of environmental and social conditions, but also as a catalyst for environmental and social improvements and as an opportunity to enhance the livability of smaller, geographically isolated historic centers, moving toward a new economy of urban reclamation.