Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Abhijeet Jadhav, Renuka Pawar; Road accident analysis and prediction. AIP Conf. Proc. 7 May 2024; 2853 (1): 020267. https://doi.org/10.1063/5.0197414 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
We propose an automotive radar system that transmits at non-uniform pulse repetition frequency (PRF) to achieve high-resolution range and Doppler estimation while transmitting sparsely along slow-time following the difference co-chirps schemes, e.g., coprime and nested chirps. At the receiver, the radar admits undersampled slow-time signals for Doppler estimation. In a single coherent processing interval (CPI), the missing Doppler samples along slow-time are interpolated via a Doppler covariance matrix that is constructed using fast-time samples. Our co-chirp joint range-Doppler estimation with Doppler de-aliasing (CoDDler) algorithm jointly estimates the range and Doppler. The Doppler spectrum obtained from the interpolated Doppler samples are utilized to de-aliase any false Doppler peaks in the sparse estimation. The proposed non-uniform PRF automotive radar provides the possibility for transmission coordination in a time division multiplexing fashion to avoid mutual interference by saving nearly 88% of time-on-target.
The aim of this study is to understand the factors that influence the attitude towards savings among Malay employees in the public sector in Malaysia. The research study has focused on both internal and external factor in understanding the influence the attitude towards savings among the employees. The internal factor in this study is savings behavior and savings culture while, the external factor is the government policy. The result of the survey that has been conducted on 769 respondents (Malay employees working in Malaysia public sector). The analysis was made using advanced multivariate statistical software of Structural Equation Modelling (SmartPLS v3). The result of the analysis found that R2 value of the model is 0.306 which indicates that the developed model has a substantial impact on the attitude towards savings. Based on the model, savings culture is the most prominent in exhibiting effect on the attitude towards savings. Keywords—government policy, savings culture, savings behavior, attitude towards savings
This study analyzes the accountability of village fund management as the only funding that is relied on, in the era of economic recession which is felt as the impact of the Covid-19 pandemic in supporting the Village SDGs policy.The study used a qualitative method with online interviews because the area of the research object was one of the black zones of the Covid-19 pandemic in Indonesia.In order to achieve accountability, the Village government avoids abuse of authority by the village head as the highest authority in the management of the Village Fund, community participation and transparency to the community in the process of managing the Village Fund and implements programs in accordance with the village typology as an independent village.Finally, the village implemented reporting procedures in accordance with applicable policies and has used SISKEUDES properly as the implementation of policy accountability.The research shows that village funds are a reliable source of funds to support the Village SDGs policy.Accountability for village fund management is a major factor that must be considered so that the sustainability of village funds is maintained.
This writing aims to determine the family planning program's service to maintain community satisfaction during the Covid-19 pandemic in BKKBN Lampung Province and find out what factors affect the services provided by the Lampung Province BKKBN. This research aims to change the mindset, work culture, and BKKBN management system of Lampung Province in public services. In contrast, this research's practical use is as information or reference material for those who need it, especially the BKKBN of Lampung Province. The research method used in this research is descriptive qualitative. Based on the data obtained, the researcher can conclude that BKKBN Lampung Province has made improvements from year to year, increased capacity, and improved Performance in Lampung Province BKKBN. However, during the Covid-19 pandemic, all services cannot be done optimally. Besides, there are still many obstacles in the form of insufficient budget, minimal supporting facilities and infrastructure, the lack of cooperation from the government and the community, making the services provided by the Lampung Province BKKBN not fully realized. Keywords—service, satisfaction, pandemics covid-19
This lecture overviews the use of drones for infrastructure inspection and maintenance. Various types of inspection, e.g., using visual cameras, LIDAR or thermal cameras are reviewed. Drone vision plays a pivotal role in drone perception/control for infrastructure inspection and maintenance, because: a) it enhances flight safety by drone localization/mapping, obstacle detection and emergency landing detection; b) performs quality visual data acquisition, and c) allows powerful drone/human interactions, e.g., through automatic event detection and gesture control. The drone should have: a) increased multiple drone decisional autonomy and b) improved multiple drone robustness and safety mechanisms (e.g., communication robustness/safety, embedded flight regulation compliance, enhanced crowd avoidance and emergency landing mechanisms). Therefore, it must be contextually aware and adaptive. Drone vision and machine learning play a very important role towards this end, covering the following topics: a) semantic world mapping b) drone and target localization, c) drone visual analysis for target/obstacle/crowd/point of interest detection, d) 2D/3D target tracking. Finally, embedded on-drone vision (e.g., tracking) and machine learning algorithms are extremely important, as they facilitate drone autonomy, e.g., in communication-denied environments. Primary application area is electric line inspection. Line detection and tracking and drone perching are examined. Human action recognition and co-working assistance are overviewed.The lecture will offer: a) an overview of all the above plus other related topics and will stress the related algorithmic aspects, such as: b) drone localization and world mapping, c) target detection d) target tracking and 3D localization e) gesture control and co-working with humans. Some issues on embedded CNN and fast convolution computing will be overviewed as well.
This article addresses risks in implementing public procurement policy in Sukabumi City.This study aims to gain further understanding of the implementation of policies, internal and external factors that analyze to create strategies to mitigate the risks in implementing public procurement policy.This research provides data and evidence on why implementation of government procurement policy is not able to be effective.The qualitative, SWOT analysis method and a litmus test are employed to explicate phenomena during the implementation of public procurement policy at Sukabumi City.Within each dimension of policy implementation, there are multiple weaknesses, including lack of communication between stakeholders, less understanding of the public procurement's regulations, lack of quality and quantity in human resources, a strong negative intervention that influencing disposition/character of procurement actors, and bureaucratic structure that has not supported in implementation.a litmus test is used to identify strategic issues.The strategy enables to mitigate administrative risks within the process of implementation of public procurement policy in Sukabumi City.Strategies can be manifested by developing procurement apps, namely: SiCamperenik; adding tender schedule and alarm to the application to ensure the tender process can be done timely; creating a roadshow program to increase the competences of procurement actors; producing guideline book regarding risks mitigation as a technical guide for public procurement actors; creating procurement local catalog to enhance role and participation of local entrepreneur; initiating commitment with civil services to create the functional position of public procurement, and creating independent Electronic Procurement Service (LPSE).
Observability and estimation are closely tied to the system structure, which can be visualized as a system graph–a graph that captures the inter-dependencies within the state variables. For example, in social system graphs such inter-dependencies represent the social interactions of different individuals. It was recently shown that contractions, a key concept from graph theory, in the system graph are critical to system observability, as (at least) one state measurement in every contraction is necessary for observability. Thus, the size and number of contractions are critical in recovering for loss of observability. In this paper, the correlation between the average-size/number of contractions and the global clustering coefficient (GCC) of the system graph is studied. Our empirical results show that estimating systems with high GCC requires fewer measurements, and in case of measurement failure, there are fewer possible options to find substitute measurement that recovers the system’s observability. This is significant as by tuning the GCC, we can improve the observability properties of large-scale engineered networks, such as social networks and smart grid.
In this paper, a resilient distributed control scheme against covert attacks for constrained multi-agent networked systems is developed. The idea consists in an adequate deployment of predictive arguments with a twofold aim: detection of malicious agent behaviors and control actions implementation to mitigate as much as possible undesirable knock-on effects.
Neural networks often require large amounts of expert annotated data to train. When changes are made in the process of medical imaging, trained networks may not perform as well, and obtaining large amounts of expert annotations for each change in the imaging process can be time consuming and expensive. Online unsupervised learning is a method that has been proposed to deal with situations where there is a domain shift in incoming data, and a lack of annotations. The aim of this study is to see whether online unsupervised learning can help COVID-19 CT scan classification models adjust to slight domain shifts, when there are no annotations available for the new data. A total of six experiments are performed using three test datasets with differing amounts of domain shift. These experiments compare the performance of the online unsupervised learning strategy to a baseline, as well as comparing how the strategy performs on different domain shifts. Code for online unsupervised learning can be found at this link: https://github.com/Mewtwo/online-unsupervised-learning
A persistent challenge to AI theories and technologies is fake news recognition which demands not only syntactic analyses of language expressions, but also their semantics comprehension. This work presents an autonomous system for fake news recognition based on a novel approach of machine semantic learning. A training-free machine learning algorithm of Differential Sentence Semantic Analyses (DSSA) is designed and implemented for fake news detection. A large set of 876 experiments randomly selected from DataCup’ 19 has demonstrated a level of 70.4% accuracy that outperforms the traditional data-driven neural network technologies normally projected at the accuracy level of 55.0%. The DSSA methodology paves a way towards autonomous, training-free, and real-time trustworthy technologies for machine knowledge learning and semantics composition.
Libraries based on social inclusion are libraries that facilitate communities to develop their potential by viewing cultural diversity, willingness to accept change, and offering opportunities to strive for, protect and advocate culture and human rights. The national library performs a transformation program for libraries based on social inclusion in an effort to improve literacy in communities. It has a purpose to create societies to thrive through the transformation of libraries based on social inclusion. More specifically the program has a feature :1). Improving the quality of library service. (2). Improving the use of services by communities according to the needs of the people. 3). Stakeholder commitment and support toward the sustainable transformation of the library. The program begins in 2018, with a coverage of 160 counties/cities with 800 villages. The program in 2020 is carried out during the pandemic and has proved to be capable of empowering various classes of society and of being the solution to economic problems. There were 10,434 community outreach activities with a total of 279,437 participants. 1,082 advocacy activities are carried out to build support and policies for the library and 147 activities are undertaken to promote the regulation that promotes the transformation of libraries. With this program, the results are: 1. There is a significant influence between library service program intervention and library progress 2. There is ahigh influence between the progress of library services and the impact (intermediate impact) felt by the community 3. This program is considered to be cost effective, which is from the cost the amount issued produces a benefit ratio of 1.2 4. This programis considered effective in terms of the time and approach used and can be extended to other areas to expand the benefits of libraries for the community. Keywords—literacy, library, Covid-19, national library of Indonesia
This paper studies the blind detection of radar pulse trains using self-convolution. The self-convolution of a horizontally polarized pulse train with a constant pulse repetition frequency (PRF) is the same as its autocorrelation, only shifted in time, provided that the pulses are symmetric. This makes the waveform amenable to blind detection even in the presence of a constant Doppler shift. Once detected, we estimate the carrier, demodulate, and estimate the PRF of the baseband train using a logarithmic frequency domain matched filter. We derive a Neyman-Pearson self-convolution detection threshold for additive white Gaussian noise (AWGN) and conduct numerical experiments to compare the Signal-to-Noise Ratio (SNR) performance against standard matched filtering. We also illustrate the logarithmic frequency matched filter’s PRF estimation accuracy.
Since their inception, AutoEncoders have been very important in representational learning. They have achieved ground-breaking results in the realm of automated unsupervised anomaly detection for various critical applications. However, anomaly detection through AutoEncoders suffers from lack of transparency when it comes to decision making based on the outputs of the AutoEncoder network, especially for image-based models. Though the residual reconstruction error map from the AutoEncoder helps explaining anomalies to a certain extent, it is not a good indicator of the implicitly learnt attributes by the model. A human interpretable explanation of why an instance is anomalous not only enables the experts to fine-tune the model but also establishes and increases trust by non-expert users of the model. Convolutional AutoEncoders in particular suffer the most as there are only limited studies that focus on transparency and explainability. In this paper, aiming to bridge this gap, we explore the feasibility and compare the performances of several State-of-the-Art Explainable Artificial Intelligence (XAI) frameworks on Convolutional AutoEncoders. The paper also aims at providing the basis for future developments of reliable and trustworthy AutoEncoders for visual anomaly detection.
This paper re-examines the concept of node equivalences like structural equivalence or automorphic equivalence, which have originally emerged in social network analysis to characterize the role an actor plays within a social system, but have since then been of independent interest for graph-based learning tasks. Traditionally, such exact node equivalences have been defined either in terms of the onehop neighborhood of a node, or in terms of the global graph structure. Here we formalize exact node roles with a scale-parameter, describing up to what distance the ego network of a node should be considered when assigning node roles – motivated by the idea that there can be local roles of a node that should not be determined by nodes arbitrarily far away in the network. We present numerical experiments that show how already “shallow” roles of depth 3 or 4 carry sufficient information to perform node classification tasks with high accuracy. These findings corroborate the success of recent graph-learning approaches that compute approximate node roles in terms of embeddings, by nonlinearly aggregating node features in an (un)supervised manner over relatively small neighborhood sizes. Indeed, based on our ideas we can construct a shallow classifier achieving on par results with recent graph neural network architectures.
Intra-Vehicular Robotics (IVR) for space exploration vehicles describes robotic capabilities to perform Intra-vehicle activity (IVA) in an autonomous or remotely operated manner. This paper focuses on autonomy, and more specifically, on the potential application of deliberation functions in robotics to enabling autonomous IVR. We provide an overview of the capabilities required to enable goal-directed operations, robotic systems’ ability to autonomously transfer a high-level goal into a set of tasks to accomplish them.
Successful introductory UAM integration into the NAS will be contingent on resilient safety systems that support reduced-crew flight operations. In this paper, we present a system that performs three functions: 1) monitors an operator’s physiological state; 2) assesses when the operator is experiencing anomalous states; and 3) mitigates risks by a combination of dynamic, context-based unilateral or collaborative dynamic function allocation of operational tasks. The monitoring process receives high data-rate sensor values from eye-tracking and electrocardiogram sensors. The assessment process takes these values and performs a classification that was developed using machine learning algorithms. The mitigation process invokes a collaboration protocol called DFACCto which, based on context, performs vehicle operations that the operator would otherwise routinely execute. This system has been demonstrated in a UAM flight simulator for an operator incapacitation scenario. The methods and initial results as well as relevant UAM and AAM scenarios will be described.
In multi-agent deep reinforcement learning, extracting sufficient and compact information of other agents is critical to attain efficient convergence and scalability of an algorithm. In canonical frameworks, distilling of such information is often done in an implicit and uninterpretable manner, or explicitly with cost functions not able to reflect the relationship between information compression and utility in representation. In this paper, we present Information-Bottleneck-based Other agents’ behavior Representation learning for Multi-agent reinforcement learning (IBORM) to explicitly seek low-dimensional mapping encoder through which a compact and informative representation relevant to other agents’ behaviors is established. IBORM leverages the information bottleneck principle to compress observation information, while retaining sufficient information relevant to other agents’ behaviors used for cooperation decision. Empirical results have demonstrated that IBORM delivers the fastest convergence rate and the best performance of the learned policies, as compared with implicit behavior representation learning and explicit behavior representation learning without explicitly considering information compression and utility.
