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arXiv 2021年

作者： Kung, Pou-Chun Wang, Chieh-Chih Lin, Wen-Chieh The Graduate Degree Program of Robotics National Yang Ming Chiao Tung University Hsinchu Taiwan The Department of Electrical and Computer Engineering National Yang Ming Chiao Tung University Taiwan The Mechanical and Mechatronics Systems Research Laboratories Industrial Technology Research Institute Hsinchu Taiwan The Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan

Radar shows great potential for autonomous driving by accomplishing long-range sensing under diverse weather conditions. But radar is also a particularly challenging sensing modality due to the radar noises. Recent works have made enormous progress in classifying free and occupied spaces in radar images by leveraging lidar label supervision. However, there are still several unsolved issues. Firstly, the sensing distance of the results is limited by the sensing range of lidar. Secondly, the performance of the results is degenerated by lidar due to the physical sensing discrepancies between the two sensors. For example, some objects visible to lidar are invisible to radar, and some objects occluded in lidar scans are visible in radar images because of the radar's penetrating capability. These sensing differences cause false positive and penetrating capability degeneration, respectively. In this paper, we propose training data preprocessing and polar sliding window inference to solve the issues. The data preprocessing aims to reduce the effect caused by radar-invisible measurements in lidar scans. The polar sliding window inference aims to solve the limited sensing range issue by applying a near-range trained network to the long-range region. Instead of using common Cartesian representation, we propose to use polar representation to reduce the shape dissimilarity between long-range and near-range data. We find that extending a near-range trained network to long-range region inference in the polar space has 4.2 times better IoU than in Cartesian space. Besides, the polar sliding window inference can preserve the radar penetrating capability by changing the viewpoint of the inference region, which makes some occluded measurements seem non-occluded for a pretrained network. Copyright © 2021, The Authors. All rights reserved.

关键词： Optical radar

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M2FN: Multi-step modality fusion for advertisement image assessment

arXiv

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arXiv 2021年

作者： Park, Kyung-Wha Ha, Jung-Woo Lee, JungHoon Kwon, Sunyoung Kim, Kyung-Min Zhang, Byoung-Tak Interdisciplinary Program in Neuroscience Seoul National University Korea Republic of NAVER AI LAB NAVER CLOVA Statistics and Actuarial Science Soongsil University Korea Republic of School of Biomedical Convergence Engineering Pusan National University Korea Republic of Department of Computer Science and Engineering Seoul National University Korea Republic of Surromind Robotics

Assessing advertisements, specifically on the basis of user preferences and ad quality, is crucial to the marketing industry. Although recent studies have attempted to use deep neural networks for this purpose, these studies have not utilized image-related auxiliary attributes, which include embedded text frequently found in ad images. We, therefore, investigated the influence of these attributes on ad image preferences. First, we analyzed large-scale real-world ad log data and, based on our findings, proposed a novel multi-step modality fusion network (M2FN) that determines advertising images likely to appeal to user preferences. Our method utilizes auxiliary attributes through multiple steps in the network, which include conditional batch normalization-based low-level fusion and attention-based high-level fusion. We verified M2FN on the AVA dataset, which is widely used for aesthetic image assessment, and then demonstrated that M2FN can achieve state-of-the-art performance in preference prediction using a real-world ad dataset with rich auxiliary attributes. © 2021, CC BY-NC-SA.

关键词： Marketing

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automotive start-stop engine based on fingerprint recognition system 1

Automotive start-stop engine based on fingerprint recognitio...

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1st International Conference on automotive, Manufacturing, and Mechanical engineering, IC-AMME 2018

作者： Rivandi, Pranoko Winda, Astuti Satrio, Dewanto Solihin, Mahmud Iwan Automotive and Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jl. K. H. Syahdan No. 9 Kemanggisan Palmerah Jakarta11480 Indonesia Faculty of Engineering Technology and Built Environment UCSI University Cheras Kuala Lumpur56000 Malaysia

Automated vehicle security system plays an important rule in nowadays advance automotive technology. One of the methods which can be applied for a security system is based on biometric identification system. Fingerprint recognition is one of the biometric systems that can be applied to the security system. In this work, fingerprint recognition system to start the motorcycle engine is developed. The fingerprint of the owner and other authorized persons will be stored into the database, then while the time of starting the engine of the vehicle, the fingerprint will be validated with the database. The minutiae extraction method is applied to find the difference of fingerprint each other after turn the image into grayscale and thinning. After the extraction, the next step is finding the ridge edge and bifurcation. The result of the image will be used as input to the Artificial Neural Network (ANN) to recognize authorized person only. The experiment of fingerprint recognition system shows that automatic start-stop engine using fingerprint recognition system based minutiae extraction and Artificial Neural Network (ANN) has accuracy 100 % and 100 %, respectively. © 2019 The Authors, published by EDP Sciences.

关键词： Neural networks

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Opportunistic Synthesis in Reactive Games under Information Asymmetry

Opportunistic Synthesis in Reactive Games under Information ...

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Annual Allerton Conference on Communication, Control, and Computing

作者： Jie Fu Department of Electrical and Computer Engineering Robotics Engineering Program Worcester Polytechnic Institute Worcester MA USA

Reactive synthesis [1]is used to synthesize a provably-correct strategy (controller) with respect to a given Linear Temporal Logic (LTL) specification in a dynamic, potentially adversarial environment. The core idea of reactive synthesis is to model the interaction between a controlled agent (called the robot) and its dynamic and uncontrollable environment as a two-player turn-based zero-sum game. In particular, players' Boolean payoffs are specified by temporal logic formulas [2]-a rich language that can expressive properties such as safety, liveness, stability, and temporally extended goals.

关键词： Games Robots Task analysis Game theory Vehicle dynamics computer security

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License plate recognition system based on principal component analysis and one-against-one multi-class support vector machine

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IOP Conference Series: Earth and Environmental Science 2020年第1期426卷

作者： Kevin Kristofer Kosasih Winda Astuti Endra Oey Automotive and Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia 11480

The license plate number which identifies the original registered place of the vehicle, as well as the usage of each vehicle which belongs to a private entity, a government vehicle, and a commercial vehicle, is identified in this work. Vehicle plate detection by a camera that mounted on parking gate. Detection process starts with collecting data using the camera. This work consists two important parts, namely image processing and classification system, respectively. In the image processing, the plate image capture by the camera is extracted to the feature space which will use as the input to the classification system. The classification system is then will identify the extracted input to the plat recognition identification. The Fast Fourier Transform (FFT) and Support Vector Machine (SVM) are applied as feature extraction technique and classification methods, respectively. The results of the training and testing phase are 80% and 66%, respectively.

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Development of Fish Separator Conveyor based on Fish Identification System

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IOP Conference Series. Earth and Environmental Science 2020年第1期426卷

作者： Muhammad Rangga Dzulfiqar Agung Elian, Adrian Adhe Astuti, Winda Muhammad Nurul Puji Automotive and Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia 11480

Indonesia as the largest archipelago and maritime country in the world has a very large marine resources. Fish is one of the marine wealthties (marine biota), which have large numbers in Indonesia. Fishermen is an important role in the process of fishing, however fish caught by fishermen are often mixed with other types of fish. Currently, the process of fist separation is done manually, which takes longer time and many error occur due to fatigue. In this work the conveyer separator based on the fish identification system is developed. The fish conveyer separator is designed and developed. The fish identification system is based on Fast Fourier Transform (FTT) technique and Artificial Neural Network (ANN). The system is working properly, since it resulting the training and testing accuracy of 80% and 66%, respectively.

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Automatic Diabetic Detection System based on Retina Using an artificial intelligent system

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IOP Conference Series: Materials Science and engineering 2020年第1期982卷

作者： Zayd Assyarif Alaydrus Winda Astuti Sofyan Tan Automotive and Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia 11480

The Diabetes measurement level usually uses the blood sugar test method, where patients have to fast for eight hours before the test. Glucose tests generally only require a small sample of blood using a sterile needle from the glucometer. The sufferer may feel pain due to a pin prick. However, not all diabetics can do glucose tests regarding having a great fear of syringes, especially in older patients. Therefore, in this work, the diabetic detection system with the retina is an alternative for early checking of patients without having to take a sample of the blood patient. The Contrast Limited Adaptive Histogram Equalization (CLAHE) is used to extract the signal, in which the extracted the retina image is later used as input to the artificial neural network (ANN) based identifier. The retina image parameters are compared and classified to identify the human retina that are intended to be performed. The results of the computer simulation show that this technique produces good accuracy 100% and 96.6%, for training and testing phases, respectively.

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LiStereo: Generate Dense Depth Maps from LIDAR and Stereo Imagery

LiStereo: Generate Dense Depth Maps from LIDAR and Stereo Im...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Junming Zhang Manikandasriram Srinivasan Ramanagopal Ram Vasudevan Matthew Johnson-Roberson Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI USA Robotics Program University of Michigan Ann Arbor MI USA Department of Mechanical Engineering University of Michigan Ann Arbor MI USA Department of Naval Architecture and Marine Engineering University of Michigan Ann Arbor MI USA

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

An accurate depth map of the environment is critical to the safe operation of autonomous robots and vehicles. Currently, either light detection and ranging (LIDAR) or stereo matching algorithms are used to acquire such depth information. However, a high-resolution LIDAR is expensive and produces sparse depth map at large range; stereo matching algorithms are able to generate denser depth maps but are typically less accurate than LIDAR at long range. This paper combines these approaches together to generate high-quality dense depth maps. Unlike previous approaches that are trained using ground-truth labels, the proposed model adopts a self-supervised training process. Experiments show that the proposed method is able to generate high-quality dense depth maps and performs robustly even with low-resolution inputs. This shows the potential to reduce the cost by using LIDARs with lower resolution in concert with stereo systems while maintaining high resolution.

关键词： Laser radar Task analysis Training Feature extraction Estimation Convolution Correlation

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Opportunistic Synthesis in Reactive Games under Information Asymmetry

Opportunistic Synthesis in Reactive Games under Information ...

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IEEE Annual Conference on Decision and Control

作者： Abhishek N. Kulkarni Jie Fu Robotics Engineering Program Worcester Polytechnic Institute Worcester MA USA Faculty of the Department of Electrical and Computer Engineering with affiliation to Robotics Engineering Program Worcester Polytechnic Institute Worcester MA USA

ISBN: (数字)9781728113982

ISBN: (纸本)9781728113999

Reactive synthesis is a class of methods to construct a provably-correct control system, referred to as a robot, with respect to a temporal logic specification in the presence of a dynamic and uncontrollable environment. This is achieved by modeling the interaction between the robot and its environment as a two-player zero-sum game. However, existing reactive synthesis methods assume both players to have complete and symmetrical information, which is not the case in many strategic interactions. In this paper, we use a variant of hypergames to model the interaction between the robot and its environment;where the latter has incomplete information about the specification of the robot. We propose a novel method of opportunistic synthesis defined over the hypergame model to identify a subset of hypergame states from where the robot can leverage the asymmetrical information to achieve a better outcome, which is not possible if both players have symmetrical and complete information. By assuming the environment to play a stochastic strategy in its perceived sure-winning and sure-losing regions of the game, we show that by following the opportunistic strategy, the robot is ensured to only improve the outcome of the game-measured by satisfaction of sub-specifications-whenever an opportunity becomes available. We demonstrate the correctness and optimality of this method using a robot motion planning example in the presence of an adversary.

关键词： Robots Games Task analysis Computational modeling Game theory Planning Stochastic processes

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Learning to Estimate Centers of Mass of Arbitrary Objects

Learning to Estimate Centers of Mass of Arbitrary Objects

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2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Sean McGovern Huitan Mao Jing Xiao Robotics Engineering Program Worcester Polytechnic Institute Department of Computer Science University of North Carolina at Charlotte

This paper introduces a reinforcement learning algorithm with robot manipulation to learn an arbitrary object's center of mass whose physical material composition is unknown. Robot learning is through manipulation of the object in a sequence of actions. The effectiveness of the algorithm is demonstrated in simulation to locate the centers of mass of rocks with complex shapes, with even or uneven mass distributions, and confirmed by vertically stacking the rocks along their learned centers of mass both in simulation and in real experiments.

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