Robust Deep Multi-Modal Sensor Fusion using Fusion Weight Regularization and Target Learning

Sensor fusion has wide applications in many domains including health care and autonomous systems. While the advent of deep learning has enabled promising multi-modal fusion of high-level features and end-to-end sensor fusion solutions, existing deep learning based sensor fusion techniques including deep gating architectures are not always resilient, leading to the issue of fusion weight inconsistency. We propose deep multi-modal sensor fusion architectures with enhanced robustness particularly under the presence of sensor failures. At the core of our gating architectures are fusion weight regularization and fusion target learning operating on auxiliary unimodal sensing networks appended to the main fusion model. The proposed regularized gating architectures outperform the existing deep learning architectures with and without gating under both clean and corrupted sensory inputs resulted from sensor failures. The demonstrated improvements are particularly pronounced when one or more multiple sensory modalities are corrupted.Comment: 8 page

Statistical analysis of safety performance of displaced left-turn intersections: Case studies in San Marcos, Texas

Displaced left-turn (DLT) intersections are designed to increase the mobility of vehicles by relocating the left-turn lane (lanes) to the far-left side of the road upstream of the main signalized intersection. Since DLT is a relatively new design and very limited crash data are available, previous studies have focused mainly on the analysis of its operational performance rather than its safety performance. To fill this gap, in this study, we investigated the safety performance of two DLT intersections located in San Marcos, Texas. Crash data from 2011 to April 2018 were extracted from the TxDOT Crash Record Information System (CRIS). These crash data were analyzed using two different approaches, i.e., statistical analysis and collision diagram-based analysis. The results of this study indicated that DLT did not increase the overall crash frequencies at the studied intersections. Traffic crashes related to left turns and right turns were reduced significantly by DLT. Meanwhile, it also caused safety issues related to traffic signage, traffic signal, geometric design, and access management at DLT intersections. Thus, in the implementation of DLT intersections, traffic engineers need to carefully consider different aspects of the DLT intersection design, including access management, traffic signal coordination, and driver acceptance. As a result of these analyses, recommendations were provided for the safe implementation of the DLT design in the future

Robot Protection in the Hazardous Environments

Rescue missions for chemical, biological, radiological, nuclear, and explosive (CBRNE) incidents are highly risky and sometimes it is impossible for rescuers to perform, while these accidents vary dramatically in features and protection requirements. The purpose of this chapter is to present several protection approaches for rescue robots in the hazardous conditions. And four types of rescue robots are presented, respectively. First, design factors and challenges of the rescue robots are analyzed and indicated for these accidents. Then the rescue robots with protective modification are presented, respectively, meeting individual hazardous requirements. And finally several tests are conducted to validate the effectiveness of these modified robots. It is clear that these well-designed robots can work efficiently for the CBRNE response activities

Task-Agnostic Learning to Accomplish New Tasks

Reinforcement Learning (RL) and Imitation Learning (IL) have made great progress in robotic control in recent years. However, these methods show obvious deterioration for new tasks that need to be completed through new combinations of actions. RL methods heavily rely on reward functions that cannot generalize well for new tasks, while IL methods are limited by expert demonstrations which do not cover new tasks. In contrast, humans can easily complete these tasks with the fragmented knowledge learned from task-agnostic experience. Inspired by this observation, this paper proposes a task-agnostic learning method (TAL for short) that can learn fragmented knowledge from task-agnostic data to accomplish new tasks. TAL consists of four stages. First, the task-agnostic exploration is performed to collect data from interactions with the environment. The collected data is organized via a knowledge graph. Compared with the previous sequential structure, the knowledge graph representation is more compact and fits better for environment exploration. Second, an action feature extractor is proposed and trained using the collected knowledge graph data for task-agnostic fragmented knowledge learning. Third, a candidate action generator is designed, which applies the action feature extractor on a new task to generate multiple candidate action sets. Finally, an action proposal is designed to produce the probabilities for actions in a new task according to the environmental information. The probabilities are then used to select actions to be executed from multiple candidate action sets to form the plan. Experiments on a virtual indoor scene show that the proposed method outperforms the state-of-the-art offline RL method: CQL by 35.28% and the IL method: BC by 22.22%.Comment: 11 pages, 11 figures, Under Revie

Response Inhibition Deficits in Insomnia Disorder: An Event-Related Potential Study With the Stop-Signal Task

Background: Response inhibition is a hallmark of executive function, which was detected impaired in various psychiatric disorders. However, whether insomnia disorder (ID) impairs response inhibition has caused great controversy.Methods: Using the auditory stop-signal paradigm coupled with event-related potentials (ERPs), we carried out this study to examine whether individuals with ID presented response inhibition deficits and further investigated the neural mechanism correlated to these deficits. Twelve individuals with ID and 13 matched good sleepers (GSs) had participated in this study, and then they performed an auditory stop-signal task (SST) in the laboratory setting with high density EEG recordings.Results: The behavioral results revealed that compared to GSs, patients with ID presented significantly longer stop-signal reaction time (SSRT), suggesting the impairment of motor inhibition among insomniacs. Their reaction time in go trials, however, showed no significant between-group difference. Considering the electrophysiological correlate underlying the longer SSRT, we found reduced P3 amplitude in patients with insomnia in the successful stop trials, which might reflect their poor efficiency of response inhibition. Finally, when we performed exploratory analyses in the failed stop and go trials, patients with ID presented reduced Pe and N1 amplitude in the failed sop trials and go trials respectively.Discussion: Taken together, these findings indicate that individuals with ID would present response inhibition deficits. Moreover, the electrophysiological correlate underlying these deficits mainly revolves around the successful stop P3 component. The present study is the first to investigate the electrophysiological correlate underlying the impaired response inhibition among insomniacs

Toward Group Applications: A Critical Review of the Classification Strategies of Lithium-Ion Batteries

To solve the problems of the decreased reliability and safety of battery pack due to the inconsistency between batteries after single batteries are grouped is of great significance to find an appropriate sorting method of single batteries. This study systematically reviews the available literature on battery sorting applications for battery researchers and users. These methods can be roughly divided into three types: direct measurement, sorting based on the model, and sorting based on the material chemistry of batteries. Among them, direct measurement is about the direct measurement of the state parameters of batteries using some professional instruments or testing tools to sort and group batteries with similar or close parameters. Sorting based on the model classifies batteries into groups by establishing a battery equivalent model and carrying out model identification and parameter estimation with machine learning or artificial intelligence algorithm. Sorting based on the material chemistry of batteries is to explore some characteristics related to the chemical mechanism inside the battery. On the basis of reading extensive literature, the methods for classification of battery are provided with an in-depth explanation, and each corresponding strengths and weaknesses of these methods are analyzed. Finally, the future developments of advanced sorting algorithms and batteries prospect. Document type: Articl

Spending and Hospital Stay for Melanoma in Hunan, China

ObjectiveThis study aimed to describe the economic burden of Chinese patients with melanoma in Hunan province of China, and to investigate the factors for hospitalization spending and length of stay (LOS) in patients undergoing melanoma surgery.MethodsData was extracted from the Chinese National Health Statistics Network Reporting System database in Hunan province during 2017–2019. Population and individual statistics were presented, and nonparametric tests and quantile regression were used to analyze the factors for spending and LOS.ResultA total of 2,644 hospitalized patients with melanoma in Hunan were identified. During 2017–2019, the total hospitalization spending was $5,247,972, and out-of-pocket payment (OOP) was$1,817,869, accounting for 34.6% of the total expenditure. The median spending was $1,123 [interquartile range (IQR):$555–2,411] per capita, and the median LOS was 10 days (IQR: 5–18). A total of 1,104 patients who underwent surgery were further analyzed. The non-parametric tests and quantile regression showed that women were associated with less spending and LOS than men. In general, patients aged 46–65 and those with lesions on the limbs had higher hospitalization costs and LOS than other subgroups.ConclusionMelanoma causes heavy economic burdens on patients in Hunan, such that the median spending is close to 60% of the averagely annual disposable income. Middle-aged men patients with melanoma on the limbs present the highest financial burden of melanoma