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The application of Raman and infrared (IR) microspectroscopy is leading to hyperspectral data containing complementary information concerning the molecular composition of a sample. The classification of hyperspectral data from the individual spectroscopic approaches is already state-of-the-art in several fields of research. However, more complex structured samples and difficult measuring conditions might affect the accuracy of classification results negatively and could make a successful classification of the sample components challenging. This contribution presents a comprehensive comparison in supervised pixel classification of hyperspectral microscopic images, proving that a combined approach of Raman and IR microspectroscopy has a high potential to improve classification rates by a meaningful extension of the feature space. It shows that the complementary information in spatially co-registered hyperspectral images of polymer samples can be accessed using different feature extraction methods and, once fused on the feature-level, is in general more accurately classifiable in a pattern recognition task than the corresponding classification results for data derived from the individual spectroscopic approaches.
In the context of the Franco-German research project Re(h)strain, this work focuses on a global system analysis integrating both safety and security analysis of international and/or urban railway stations. The Re(h)strain project focuses on terrorist attacks on high speed train systems and investigates prevention and mitigation measures to reduce the overall vulnerability and strengthen the system resilience. One main criterion regarding public transport issues is the number of passengers. For example, the railway station of Paris “Gare du Nord” deals with a bigger number of passengers than the biggest airport in the world (SNCF open Data 2014), the Atlanta airport, but in terms of passengers, it is only around the 23rd rank railway station in the world. Due to the enormous mass of people, this leads to the system approach of breaking out the station into several classes of zones, e.g. entrance, main hall, quays, trains, etc. All classes are analysed considering state-of-the-art parameters, like targets attractiveness, feasibility of attack, possible damage, possible mitigation and defences. Then, safety incidence of security defence is discussed in order to refine security requirement with regard to the considered zone. Finally, global requirements of security defence correlated to the corresponding class of zones are proposed.
Entering the work envelope of an industrial robot can lead to severe injury from collisions with moving parts of the system. Conventional safety mechanisms therefore mostly restrict access to the robot using physical barriers such as walls and fences or non-contact protective devices including light curtains and laser scanners. As none of these mechanisms applies to human-robot-collaboration (HRC), a concept in which human and machine complement one another by working hand in hand, there is a rising need for safe and reliable detection of human body parts amidst background clutter. For this application camera-based systems are typically well suited. Still, safety concerns remain, owing to possible detection failures caused by environmental occlusion, extraneous light or other adverse imaging conditions. While ultrasonic proximity sensing can provide physical diversity to the system, it does not yet allow to reliably distinguish relevant objects from background objects.This work investigates a new approach to detecting relevant objects and human body parts based on acoustic holography. The approach is experimentally validated using a low-cost application-specific ultrasonic sensor system created from micro-electromechanical systems (MEMS). The presented results show that this system far outperforms conventional proximity sensors in terms of lateral imaging resolution and thus allows for more intelligent muting processes without compromising the safety of people working close to the robot. Based upon this work, a next step could be the development of a multimodal sensor systems to safeguard workers who collaborate with robots using the described ultrasonic sensor system.
The detection of human skin in images is a very desirable feature for applications such as biometric face recognition, which is becoming more frequently used for, e.g., automated border or access control. However, distinguishing real skin from other materials based on imagery captured in the visual spectrum alone and in spite of varying skin types and lighting conditions can be dicult and unreliable. Therefore, spoofing attacks with facial disguises or masks are still a serious problem for state of the art face recognition algorithms. This dissertation presents a novel approach for reliable skin detection based on spectral remission properties in the short-wave infrared (SWIR) spectrum and proposes a cross-modal method that enhances existing solutions for face verification to ensure the authenticity of a face even in the presence of partial disguises or masks. Furthermore, it presents a reference design and the necessary building blocks for an active multispectral camera system that implements this approach, as well as an in-depth evaluation. The system acquires four-band multispectral images within T = 50ms. Using a machine-learning-based classifier, it achieves unprecedented skin detection accuracy, even in the presence of skin-like materials used for spoofing attacks. Paired with a commercial face recognition software, the system successfully rejected all evaluated attempts to counterfeit a foreign face.
A deployment of the Vehicle-2-Vehicle communication technology according to ETSI is in preparation in Europe. Currently, a policy for a necessary Public Key Infrastructure to enrol cryptographic keys and certificates for vehicles and infrastructure component is in discussion to enable an interoperable Vehicle-2-Vehicle communication. Vehicle-2-Vehicle communication means that vehicles periodically send Cooperative Awareness Messages. These messages contain the current geographic position, driving direction, speed, acceleration, and the current time of a vehicle. To protect privacy (location privacy, “speed privacy”) of vehicles and drivers ETSI provides a specific pseudonym concept. We show that the Vehicle-2-Vehicle communication can be misused by an attacker to plot a trace of sequent Cooperative Awareness Messages and to link this trace to a specific vehicle. Such a trace is non-disputable due to the cryptographic signing of the messages. So, the periodically sending of Cooperative Awareness Messages causes privacy problems even if the pseudonym concept is applied.
A deployment of the Vehicle-to-Vehicle communication technology according to ETSI is in preparation in Europe. Currently, a Public Key Infrastructure policy for Intelligent Transport Systems in Europe is in discussion to enable V2V communication. This policy set aside two classes of keys and certificates for ITS vehicle stations: long term authentication keys and pseudonymous keys and certificates. We show that from our point of view the periodic sent Cooperative Awareness Messages with extensive data have technical limitations and together with the pseudonym concept cause privacy problems.
Persons entering the working range of industrial robots are exposed to a high risk of collision with moving parts of the system, potentially causing severe injuries. Conventional systems, which restrict the access to this area, range from walls and fences to light barriers and other vision based protective devices (VBPD). None of these systems allow to distinguish between humans and workpieces in a safe and reliable manner. In this work, a new approach is investigated, which uses an active near-infrared (NIR) camera system with advanced capabilities of skin detection to distinguish humans from workpieces based on characteristic spectral signatures. This approach allows to implement more intelligent muting processes and at the same time increases the safety of persons working close to the robots. The conceptual integration of such a camera system into a VBPD and the enhancement of person detection methods through skin detection are described and evaluated in this paper. Based upon this work, next steps could be the development of multimodal sensor systems to safeguard working ranges of collaborating robots using the described camera system.
The proper use of protective hoods on panel saws should reliably prevent severe injuries from (hand) contact with the blade or material kickbacks. It also should minimize long-term lung damages from fine-particle pollution. To achieve both purposes the hood must be adjusted properly by the operator for each workpiece to fit its height. After a work process is finished, the hood must be lowered down completely to the bench. Unfortunately, in practice the protective hood is fixed at a high position for most of the work time and herein loses its safety features. A system for an automatic height adjustment of the hood would increase comfort and safety. If the system can distinguish between workpieces and skin reliably, it furthermore will reduce occupational hazards for panel saw users. A functional demonstrator of such a system has been designed and implemented to show the feasibility of this approach. A specific optical sensor system is used to observe a point on the extended cut axis in front of the blade. The sensor determines the surface material reliably and measures the distance to the workpiece surface simultaneously. If the distance changes because of a workpiece fed to the machine, the control unit will set the motor-adjusted hood to the correct height. If the sensor detects skin, the hood will not be moved. In addition a camera observes the area under the hood. If there are no workpieces or offcuts left under the hood, it will be lowered back to the default position.
The device (10) has a handrail (18) provided with an optical contactless monitoring device formed as an active sensor system, where the monitoring device is arranged in a region of a guide (14) of the handrail at a front base (16) of an escalator (12) or a moving pavement. The monitoring device has two transmission paths (28, 30) with wavelength bands that are different from each other, where one of the paths includes the handrail. Ratio or difference between signals of the paths is used for recognizing foreign bodies e.g. hands of adults and children.
At previous SIAS conferences, we presented a novel opto-electronic safety sensor system for skin detection at circular saws jointly developed with the Institute for Occupational Safety and Health of the German Social Accident Insurance (IFA). This work now presents the development results of our consecutive research on a prototype of a sensor system for more general production machine applications including robot workplaces. The system uses offthe shelf LEDs and photodiodes in combination with dedicated optics and a microcontroller system to implement a so-called spectral light curtain.
Microcontroller-based sensor systems offer great opportunities for the implementation of safety features for potentially dangerous machinery. However, in general they are difficult to assess with regard to their reliability and failure rate. This paper describes the safety assessment of hardware and software of a new and innovative sensor system. The hardware is assessed by standardized methods according to norm EN ISO 13849-1, while the use of model checking is presented as an approach to solve the problem of validating the software.
In this paper, we introduce an optical sensor system, which is integrated into an industrial push-button. The sensor allows to classify the type of material that is in contact with the button when pressed into different material categories on the basis of the material's so called "spectral signature". An approach for a safety sensor system at circular table saws on the same base has been introduced previously on SIAS-2007. This contactless working sensor is able to distinguish reliably between skin, textiles, leather and various other kinds of materials. A typical application for this intelligent push-button is the use at possibly dangerous machines, whose operating instructions include either the prohibition or the obligation to wear gloves during the work at the machine. An exemple of machines at which no gloves are allowed are pillar drilling machines, because of the risk of getting caught in the drill chuck and being turned in by the machine. In many cases this causes very serious hand injuries. Depending on the application needs, the sensor system integrated into the push-button can be configured flexibly by software to prevent the operator from accidentally starting a machine with or without gloves, which can decrease the risk of severe accidents significantly. Especially two-hand controls are incentive to manipulation for easier handling. By equipping both push-buttons of a two-hand control with material classification properties, the user is forced to operate the controls with his bare fingers. That limitation disallows the manipulation of a two-hand control by a simple rodding device.