scholarly journals Free Space Detection Using Camera-LiDAR Fusion in a Bird’s Eye View Plane

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7623
Author(s):  
Byeongjun Yu ◽  
Dongkyu Lee ◽  
Jae-Seol Lee ◽  
Seok-Cheol Kee
Keyword(s):  
Network Architecture ◽  
Empirical Studies ◽  
Target Object ◽  
Range Data ◽  
Road Detection ◽  
Long Distance ◽  
Wide Range ◽  
Additional Domain ◽  
Institute Of Technology ◽  
Domain Transformation

Although numerous road segmentation studies have utilized vision data, obtaining robust classification is still challenging due to vision sensor noise and target object deformation. Long-distance images are still problematic because of blur and low resolution, and these features make distinguishing roads from objects difficult. This study utilizes light detection and ranging (LiDAR), which generates information that camera images lack, such as distance, height, and intensity, as a reliable supplement to address this problem. In contrast to conventional approaches, additional domain transformation to a bird’s eye view space is executed to obtain long-range data with resolutions comparable to those of short-range data. This study proposes a convolutional neural network architecture that processes data transformed to a bird’s eye view plane. The network’s pathways are split into two parts to resolve calibration errors in the transformed image and point cloud. The network, which has modules that operate sequentially at various scaled dilated convolution rates, is designed to quickly and accurately handle a wide range of data. Comprehensive empirical studies using the Karlsruhe Institute of Technology and Toyota Technological Institute’s (KITTI’s) road detection benchmarks demonstrate that this study’s approach takes advantage of camera and LiDAR information, achieving robust road detection with short runtimes. Our result ranks 22nd in the KITTI’s leaderboard and shows real-time performance.

scholarly journals Low Power Cusped Field Thruster Developed for the Space-Borne Gravitational Wave Detection Mission in China

2021 ◽  
Vol 11 (14) ◽  
pp. 6549
Author(s):  
Hui Liu ◽  
Ming Zeng ◽  
Xiang Niu ◽  
Hongyan Huang ◽  
Daren Yu
Keyword(s):  
Low Power ◽  
Gravitational Wave ◽  
Attitude Control ◽  
High Efficiency ◽  
Experimental Investigations ◽  
Attitude Control System ◽  
Gravitational Wave Detection ◽  
Wave Detection ◽  
Wide Range ◽  
Institute Of Technology

The microthruster is the crucial device of the drag-free attitude control system, essential for the space-borne gravitational wave detection mission. The cusped field thruster (also called the High Efficiency Multistage Plasma Thruster) becomes one of the candidate thrusters for the mission due to its low complexity and potential long life over a wide range of thrust. However, the prescribed minimum of thrust and thrust noise are considerable obstacles to downscaling works on cusped field thrusters. This article reviews the development of the low power cusped field thruster at the Harbin Institute of Technology since 2012, including the design of prototypes, experimental investigations and simulation studies. Progress has been made on the downscaling of cusped field thrusters, and a new concept of microwave discharge cusped field thruster has been introduced.

scholarly journals Grasp to See—Object Classification Using Flexion Glove with Support Vector Machine

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1461
Author(s):  
Shun-Hsin Yu ◽  
Jen-Shuo Chang ◽  
Chia-Hung Dylan Tsai
Keyword(s):  
Support Vector Machine ◽  
Daily Life ◽  
Object Classification ◽  
Target Object ◽  
The Other ◽  
Support Vector ◽  
Wide Range ◽  
Dark Space ◽  
Flex Sensors ◽  
Shape And Size

This paper proposes an object classification method using a flexion glove and machine learning. The classification is performed based on the information obtained from a single grasp on a target object. The flexion glove is developed with five flex sensors mounted on five finger sleeves, and is used for measuring the flexion of individual fingers while grasping an object. Flexion signals are divided into three phases, and they are the phases of picking, holding and releasing, respectively. Grasping features are extracted from the phase of holding for training the support vector machine. Two sets of objects are prepared for the classification test. One is printed-object set and the other is daily-life object set. The printed-object set is for investigating the patterns of grasping with specified shape and size, while the daily-life object set includes nine objects randomly chosen from daily life for demonstrating that the proposed method can be used to identify a wide range of objects. According to the results, the accuracy of the classifications are achieved 95.56% and 88.89% for the sets of printed objects and daily-life objects, respectively. A flexion glove which can perform object classification is successfully developed in this work and is aimed at potential grasp-to-see applications, such as visual impairment aid and recognition in dark space.

scholarly journals A developmentally descriptive method for quantifying shape in gastropod shells

2020 ◽  
Vol 17 (163) ◽  
pp. 20190721
Author(s):  
J. Larsson ◽  
A. M. Westram ◽  
S. Bengmark ◽  
T. Lundh ◽  
R. K. Butlin
Keyword(s):  
Empirical Studies ◽  
Three Dimensional ◽  
Shell Shape ◽  
Large Set ◽  
Littorina Saxatilis ◽  
Shape Variation ◽  
Gastropod Shell ◽  
Geometric Morphometric ◽  
Wide Range ◽  
Descriptive Method

The growth of snail shells can be described by simple mathematical rules. Variation in a few parameters can explain much of the diversity of shell shapes seen in nature. However, empirical studies of gastropod shell shape variation typically use geometric morphometric approaches, which do not capture this growth pattern. We have developed a way to infer a set of developmentally descriptive shape parameters based on three-dimensional logarithmic helicospiral growth and using landmarks from two-dimensional shell images as input. We demonstrate the utility of this approach, and compare it to the geometric morphometric approach, using a large set of Littorina saxatilis shells in which locally adapted populations differ in shape. Our method can be modified easily to make it applicable to a wide range of shell forms, which would allow for investigations of the similarities and differences between and within many different species of gastropods.

scholarly journals Personality trait differences across types of entrepreneurs: a systematic literature review

2021 ◽  
Author(s):  
Florentine U. Salmony ◽  
Dominik K. Kanbach
Keyword(s):  
Literature Review ◽  
Personality Traits ◽  
Systematic Literature Review ◽  
Empirical Studies ◽  
Academic Research ◽  
Future Research ◽  
Rural Farmers ◽  
Start Up ◽  
Wide Range ◽  
Research Integration

AbstractThe personality traits that define entrepreneurs have been of significant interest to academic research for several decades. However, previous studies have used vastly different definitions of the term “entrepreneur”, meaning their subjects have ranged from rural farmers to tech-industry start-up founders. Consequently, most research has investigated disparate sub-types of entrepreneurs, which may not allow for inferences to be made regarding the general entrepreneurial population. Despite this, studies have frequently extrapolated results from narrow sub-types to entrepreneurs in general. This variation in entrepreneur samples reduces the comparability of empirical studies and calls into question the reviews that pool results without systematic differentiation between sub-types. The present study offers a novel account by differentiating between the definitions of “entrepreneur” used in studies on entrepreneurs’ personality traits. We conduct a systematic literature review across 95 studies from 1985 to 2020. We uncover three main themes across the previous studies. First, previous research applied a wide range of definitions of the term “entrepreneur”. Second, we identify several inconsistent findings across studies, which may at least partially be due to the use of heterogeneous entrepreneur samples. Third, the few studies that distinguished between various types of entrepreneurs revealed differences between them. Our systematic differentiation between entrepreneur sub-types and our research integration offer a novel perspective that has, to date, been widely neglected in academic research. Future research should use clearly defined entrepreneurial samples and conduct more systematic investigations into the differences between entrepreneur sub-types.

Book Reviews

2015 ◽  
Vol 53 (4) ◽  
pp. 1033-1036
Keyword(s):  
Cost Benefit Analysis ◽  
Empirical Studies ◽  
Cost Benefit ◽  
Well Being ◽  
Chicago School ◽  
Associate Professor ◽  
Law School ◽  
Hedonic Theory ◽  
The Law ◽  
Institute Of Technology

Matthew D. Adler of Duke University reviews “Happiness and the Law”, by John Bronsteen, Christopher Buccafusco, and Jonathan S. Masur. The Econlit abstract of this book begins: “Assesses how the law affects people's quality of life with a particular focus on criminal punishment and civil lawsuits. Discusses measuring happiness; well-being analysis; well-being analysis versus cost–benefit analysis; happiness and punishment; adaptation, affective forecasting, and civil litigation; some problems with preference theories and objective theories; a hedonic theory of well-being; addressing objections to the hedonic theory; and the future of happiness and the law. Bronsteen is a professor in the Loyola University Chicago School of Law. Buccafusco is an associate professor in the Chicago-Kent School of Law and Codirector of the Center for Empirical Studies of Intellectual Property at the Illinois Institute of Technology. Masur is John P. Wilson Professor of Law in the University of Chicago Law School.”

Assessment of needs for care among patients with schizophrenic disorders 15 and 17 years after first onset of psychosis

1997 ◽  
Vol 6 (S1) ◽  
pp. 21-28 ◽  
Author(s):  
Durk Wiersma ◽  
Fokko J. Nienhuis ◽  
Cees J. Slooff ◽  
Robert Giel ◽  
Aant De Jong
Keyword(s):  
Mental Disorders ◽  
Empirical Studies ◽  
Signs And Symptoms ◽  
Expected Improvement ◽  
Chronic Patients ◽  
Cross Sectional ◽  
Wide Range ◽  
Social Disablement ◽  
First Onset

Severe and long term mental disorders, like schizophrenia, show in general a wide range of psychiatric signs and symptoms, psychological and physiological impairments and social disablement (Shepherd, 1994; Wing, 1982) reflecting a variety of mental health needs. Many studies provide only a cross-sectional view of the clinical and social problems of the patient population, for example at intake or admission to a mental hospital. Longitudinal studies following patients after discharge for some period of months or years show in general the expected improvement of functioning (e.g. Nienhuis et al., 1994), but as far as only chronic patients are concerned such a positive change is much less noted. The concept of chronicity of mental disorders would presume that after some time needs are fairly predictable and stable and do not change much over time. Our investigation on the long-term course of schizophrenia (Wiersma et al., 1996; 1997) enables us to study over a period of two years, from 15 to 17 years since first onset of psychosis, the stability or variability of needs in schizophrenic disorder. We are not aware of empirical studies on changes in needs among patients with long-term disorders.

Evaluation of Belleville Springs and Damping Through Static Cyclic Loading “Hysteresis” for pVARD Optimization: Part-I

2021 ◽  
Author(s):  
Abdelsalam Abugharara ◽  
Stephen Butt
Keyword(s):  
Data Analysis ◽  
Cyclic Loading ◽  
Dynamic Compression ◽  
Range Data ◽  
Compression Tests ◽  
Rotary Drilling ◽  
Static Compression ◽  
Drilling Performance ◽  
Wide Range ◽  
Compression Hysteresis

Abstract One unconventional application that researchers have been investigating for enhancing drilling performance, has been implemented through improving and stabilizing the most effective downhole drilling parameters including (i) increasing downhole dynamic weight on bit (DDWOB), (ii) stabilizing revolution per minutes (rpm), (iii) minimizing destructive downhole vibrations, among many others. As one portion of a three-part-research that consists of a comprehensive data analysis and evaluation of a static compression hysteresis, dynamic compression hysteresis, and corresponding drilling tests, this research investigates through static cyclic loading “Hysteresis” of individual and combined springs and damping the functionality of the passive Vibration Assisted Rotary Drilling (pVARD) tool that could be utilized towards enhancing the drilling performance. Tests are conducted on the two main pVARD tool sections that include (i) Belleville springs, which represent the elasticity portion and (ii) the damping section, which represents the viscous portion. Firstly, tests were conducted through static cyclic loading “Hysteresis” of (i) a mono elastic, (ii) a mono viscus, and (iii) dual elastic-viscus cyclic loading scenarios for the purpose of further examining pVARD functionality. For performing static compression tests, a calibrated geomechanics loading frame was utilized, and various spring stacking of different durometer damping were tested to seek a wide-range data and to provide a multi-angle analysis. Results involved analyzing loading and displacement relationships of individual and combined springs and damping are presented with detailed report of data analysis, discussion, and conclusions.

Social efficiency of internal affairs bodies

2021 ◽  
Author(s):  
Egor Bunov
Keyword(s):  
Law Enforcement ◽  
Empirical Studies ◽  
Multidimensional Analysis ◽  
Law Enforcement Agencies ◽  
Social Effectiveness ◽  
The Social ◽  
Wide Range ◽  
Degree Of Satisfaction ◽  
Internal Affairs

The monograph contains a theoretical analysis of the social effectiveness of the internal affairs bodies as the degree of satisfaction of the population with the quality of law enforcement activities to protect their interests, rights and freedoms. The results of a multidimensional analysis of empirical studies of the influence of macro - and microsocial factors on the effectiveness of interaction between the population and law enforcement agencies are presented. The article substantiates the criteria for social assessment of the activities of the internal affairs bodies, the use of which allows for practical adjustment of the forms and methods of the management system. For a wide range of readers interested in the practice of applying legal measures of law enforcement.

scholarly journals TSAL: Two steps Adversarial learning based domain adaptation

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Haidi Hasan Badr ◽  
Nayer Mahmoud Wanas ◽  
Magda Fayek
Keyword(s):  
Network Architecture ◽  
Domain Adaptation ◽  
Data Availability ◽  
Fine Tuning ◽  
Target Domain ◽  
Adversarial Learning ◽  
Prior Art ◽  
Real World Problem ◽  
Wide Range ◽  
Benchmark Datasets

Since labeled data availability differs greatly across domains, Domain Adaptation focuses on learning in new and unfamiliar domains by reducing distribution divergence. Recent research suggests that the adversarial learning approach could be a promising way to achieve the domain adaptation objective. Adversarial learning is a strategy for learning domain-transferable features in robust deep networks. This paper introduces the TSAL paradigm, a two-step adversarial learning framework. It addresses the real-world problem of text classification, where source domain(s) has labeled data but target domain (s) has only unlabeled data. TSAL utilizes joint adversarial learning with class information and domain alignment deep network architecture to learn both domain-invariant and domain-specific features extractors. It consists of two training steps that are similar to the paradigm, in which pre-trained model weights are used as initialization for training with new data. TSAL’s two training phases, however, are based on the same data, not different data, as is the case with fine-tuning. Furthermore, TSAL only uses the learned domain-invariant feature extractor from the first training as an initialization for its peer in subsequent training. By doubling the training, TSAL can emphasize the leverage of the small unlabeled target domain and learn effectively what to share between various domains. A detailed analysis of many benchmark datasets reveals that our model consistently outperforms the prior art across a wide range of dataset distributions.

scholarly journals BUILDING CHANGE DETECTION FROM BITEMPORAL AERIAL IMAGES USING DEEP LEARNING

2020 ◽  
Vol V-2-2020 ◽  
pp. 565-571
Author(s):  
S. Su ◽  
T. Nawata ◽  
T. Fuse
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Urban Areas ◽  
Network Architecture ◽  
Aerial Images ◽  
Detection Accuracy ◽  
Detection Model ◽  
Wide Range ◽  
Surface Models ◽  
Data Source

Abstract. Automatic building change detection has become a topical issue owing to its wide range of applications, such as updating building maps. However, accurate building change detection remains challenging, particularly in urban areas. Thus far, there has been limited research on the use of the outdated building map (the building map before the update, referred to herein as the old-map) to increase the accuracy of building change detection. This paper presents a novel deep-learning-based method for building change detection using bitemporal aerial images containing RGB bands, bitemporal digital surface models (DSMs), and an old-map. The aerial images have two types of spatial resolutions, 12.5 cm or 16 cm, and the cell size of the DSMs is 50 cm × 50 cm. The bitemporal aerial images, the height variations calculated using the differences between the bitemporal DSMs, and the old-map were fed into a network architecture to build an automatic building change detection model. The performance of the model was quantitatively and qualitatively evaluated for an urban area that covered approximately 10 km2 and contained over 21,000 buildings. The results indicate that it can detect the building changes with optimum accuracy as compared to other methods that use inputs such as i) bitemporal aerial images only, ii) bitemporal aerial images and bitemporal DSMs, and iii) bitemporal aerial images and an old-map. The proposed method achieved recall rates of 89.3%, 88.8%, and 99.5% for new, demolished, and other buildings, respectively. The results also demonstrate that the old-map is an effective data source for increasing building change detection accuracy.

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