Logistik-Leitstände in Industrieunternehmen

2018 ◽  
Vol 59 (2) ◽  
pp. 15-20
Author(s):  
Jakob Boos ◽  
Torsten Czenskowsky ◽  
Jürgen Harland ◽  
Carsten Wagner
Keyword(s):  
Real Time ◽  
Steel Industry ◽  
Processing Time ◽  
Logistics Management ◽  
German Industry ◽  
Logistics Control

Dieser Artikel beschäftigt sich mit dem Aufbau von Logistik-Leitständen in deutschen Industrieunternehmen. Leitstände werden zur strategieorientierten Unterstützung des Logistik-Managements im Rahmen des Logistikcontrollings aufgebaut, um in Echtzeit auf Transport- und Verladeprozesse einzuwirken. Im erörterten Beispiel aus der deutschen Stahlindustrie konnte in den ersten sechs Monaten nach Einführung des Logistik-Leitstands die Termintreue verbessert sowie die Durchlaufzeit bei der Verladung gesenkt werden. Dies ist darauf zurückzuführen, dass ein Logistik-Leitstand mehr Transparenz über die zur Verfügung stehenden Transport-, Lager- und Verladekapazitäten erzeugt und bei Abweichungen zügiger eingegriffen werden kann. This article deals with the introduction of logistics control panels (or logistics cockpits) in the German industry. Control panels are applied as a strategy-oriented support for logistics management in line with the logistics controlling, in order to react to the transport and loading processes in real time. In an example from the German steel industry, there was an improvement in the adherence to schedules in the first six months after the implementation of a logistics cockpit. The means of transportation processing time could be reduced as well. This can be attributed to a logistics control panel‘s increased transparency regarding transport, storage and loading capacities and to a faster possible reaction to deviations. Keywords: technisch automatisiertes, strategisches oder individuelles pricing, lebensmitteleinzelhandel, dynamische preisgestaltung

An efficient solution for fast generation of multi-GNSS real-time products

2021 ◽  
Author(s):  
Hongjie Zheng ◽  
Hanyu Chang ◽  
Yongqiang Yuan ◽  
Qingyun Wang ◽  
Yuhao Li ◽  
...  
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Processing Time ◽  
Efficient Solution ◽  
Gpu Computing ◽  
Sampling Rate ◽  
Precise Orbit Determination ◽  
Processing Unit ◽  
Processing Efficiency ◽  
Central Processing

<p>Global navigation satellite systems (GNSS) have been playing an indispensable role in providing positioning, navigation and timing (PNT) services to global users. Over the past few years, GNSS have been rapidly developed with abundant networks, modern constellations, and multi-frequency observations. To take full advantages of multi-constellation and multi-frequency GNSS, several new mathematic models have been developed such as multi-frequency ambiguity resolution (AR) and the uncombined data processing with raw observations. In addition, new GNSS products including the uncalibrated phase delay (UPD), the observable signal bias (OSB), and the integer recovery clock (IRC) have been generated and provided by analysis centers to support advanced GNSS applications.</p><p>       However, the increasing number of GNSS observations raises a great challenge to the fast generation of multi-constellation and multi-frequency products. In this study, we proposed an efficient solution to realize the fast updating of multi-GNSS real-time products by making full use of the advanced computing techniques. Firstly, instead of the traditional vector operations, the “level-3 operations” (matrix by matrix) of Basic Liner Algebra Subprograms (BLAS) is used as much as possible in the Least Square (LSQ) processing, which can improve the efficiency due to the central processing unit (CPU) optimization and faster memory data transmission. Furthermore, most steps of multi-GNSS data processing are transformed from serial mode to parallel mode to take advantage of the multi-core CPU architecture and graphics processing unit (GPU) computing resources. Moreover, we choose the OpenBLAS library for matrix computation as it has good performances in parallel environment.</p><p>       The proposed method is then validated on a 3.30 GHz AMD CPU with 6 cores. The result demonstrates that the proposed method can substantially improve the processing efficiency for multi-GNSS product generation. For the precise orbit determination (POD) solution with 150 ground stations and 128 satellites (GPS/BDS/Galileo/GLONASS/QZSS) in ionosphere-free (IF) mode, the processing time can be shortened from 50 to 10 minutes, which can guarantee the hourly updating of multi-GNSS ultra-rapid orbit products. The processing time of uncombined POD can also be reduced by about 80%. Meanwhile, the multi-GNSS real-time clock products can be easily generated in 5 seconds or even higher sampling rate. In addition, the processing efficiency of UPD and OSB products can also be increased by 4-6 times.</p>

scholarly journals Real-Time Monitoring of Jet Trajectory during Jetting Based on Near-Field Computer Vision

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 690 ◽  
Author(s):  
Jinsong Zhu ◽  
Wei Li ◽  
Da Lin ◽  
Ge Zhao
Keyword(s):  
Computer Vision ◽  
Prediction Model ◽  
Real Time ◽  
Processing Time ◽  
Near Field ◽  
Image Correction ◽  
Noise Elimination ◽  
Sensor Device ◽  
Jet Trajectory ◽  
Novel Method

A novel method of near-field computer vision (NFCV) was developed to monitor the jet trajectory during the jetting process, which was used to precisely predict the falling point position of the jet trajectory. By means of a high-resolution webcam, the NFCV sensor device collected near-field images of the jet trajectory. Preprocessing of collected images was carried out, which included squint image correction, noise elimination, and jet trajectory extraction. The features of the jet trajectory in the processed image were extracted, including: start-point slope (SPS), end-point slope (EPS), and overall trajectory slope (OTS) based on the proposed mean position method. A multiple regression jet trajectory range prediction model was established based on these trajectory characteristics and the reliability of the model was verified. The results show that the accuracy of the prediction model is not less than 94% and the processing time is less than 0.88s, which satisfy the requirements of real-time online jet trajectory monitoring.

ENPP: Extended Non-preemptive PP-aware Scheduling for Real-time Cloud Services

2016 ◽  
Vol 6 (5) ◽  
pp. 2291
Author(s):  
Fereshteh Hoseini ◽  
Mostafa Ghobaei Arani ◽  
Alireza Taghizadeh
Keyword(s):  
Resource Allocation ◽  
Real Time ◽  
Processing Time ◽  
Resource Constraints ◽  
Cloud Services ◽  
Scheduling Problems ◽  
Hard Problems ◽  
Real Time Applications ◽  
Simulation Results ◽  
Np Hard Problems

<p class="Abstract">By increasing the use of cloud services and the number of requests to processing tasks with minimum time and costs, the resource allocation and scheduling, especially in real-time applications become more challenging. The problem of resource scheduling, is one of the most important scheduling problems in the area of NP-hard problems. In this paper, we propose an efficient algorithm is proposed to schedule real-time cloud services by considering the resource constraints. The simulation results show that the proposed algorithm shorten the processing time of tasks and decrease the number of canceled tasks.</p>

scholarly journals Real-time Neural Networks Implementation Proposal for Microcontrollers

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1597
Author(s):  
Caio José B. V. Guimarães ◽  
Marcelo A. C. Fernandes
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Intelligent Systems ◽  
Implementation Strategy ◽  
Processing Time ◽  
Mean Squared Error ◽  
Low Cost ◽  
Real Time Applications ◽  
Time Required ◽  
Process Classification

The adoption of intelligent systems with Artificial Neural Networks (ANNs) embedded in hardware for real-time applications currently faces a growing demand in fields such as the Internet of Things (IoT) and Machine to Machine (M2M). However, the application of ANNs in this type of system poses a significant challenge due to the high computational power required to process its basic operations. This paper aims to show an implementation strategy of a Multilayer Perceptron (MLP)-type neural network, in a microcontroller (a low-cost, low-power platform). A modular matrix-based MLP with the full classification process was implemented as was the backpropagation training in the microcontroller. The testing and validation were performed through Hardware-In-the-Loop (HIL) of the Mean Squared Error (MSE) of the training process, classification results, and the processing time of each implementation module. The results revealed a linear relationship between the values of the hyperparameters and the processing time required for classification, also the processing time concurs with the required time for many applications in the fields mentioned above. These findings show that this implementation strategy and this platform can be applied successfully in real-time applications that require the capabilities of ANNs.

SVM-BASED PHONEME CLASSIFICATION AND LIP SHAPE REFINEMENT IN REAL-TIME LIP-SYNCH SYSTEM

2006 ◽  
Vol 20 (07) ◽  
pp. 1029-1051 ◽  
Author(s):  
HANSEOK KO ◽  
DAVID K. HAN
Keyword(s):  
Real Time ◽  
Processing Time ◽  
Support Vector ◽  
Mel Frequency Cepstral Coefficients ◽  
Phoneme Classification ◽  
Second Stage ◽  
Single Frame ◽  
Time Operation ◽  
Real Time Operation ◽  
Coarse To Fine

In this paper, we present a real time lip-synch system that activates 2-D avatar's lip motion in synch with incoming speech utterance. To achieve the real time operation of the system, the processing time was minimized by "merge and split" procedures resulting in coarse-to-fine phoneme classification. At each stage of phoneme classification, the support vector machine (SVM) method was applied to reduce the computational load while maintaining the desired accuracy. The coarse-to-fine phoneme classification, is accomplished via two_stages of feature extraction: in the first stage, each speech frame is acoustically analyzed for three classes of lip opening using Mel Frequency Cepstral Coefficients (MFCC) as a feature; in the second stage, each frame is further refined for detailed lip shape using formant information. The method was implemented in 2-D lip animation and it was demonstrated that the system was effective in accomplishing real-time lip-synch. This approach was tested on a PC using the Microsoft Visual Studio with an Intel Pentium IV 1.4 Giga Hz CPU and 384 MB RAM. It was observed that the methods of phoneme merging and SVM achieved about twice the speed in recognition than the method employing the Hidden Markov Model (HMM). A typical latency time per a single frame observed using the proposed method was in the order of 18.22 milliseconds while an HMM method under identical conditions resulted about 30.67 milliseconds.

scholarly journals A Real-Time Semantic Segmentation Method of Sheep Carcass Images Based on ICNet

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Shida Zhao ◽  
Guangzhao Hao ◽  
Yichi Zhang ◽  
Shucai Wang
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Processing Time ◽  
Image Data ◽  
Semantic Segmentation ◽  
Segmentation Method ◽  
Single Image ◽  
Evaluation Standard ◽  
Segmentation Accuracy ◽  
Image Dataset

How to realize the accurate recognition of 3 parts of sheep carcass is the key to the research of mutton cutting robots. The characteristics of each part of the sheep carcass are connected to each other and have similar features, which make it difficult to identify and detect, but with the development of image semantic segmentation technology based on deep learning, it is possible to explore this technology for real-time recognition of the 3 parts of the sheep carcass. Based on the ICNet, we propose a real-time semantic segmentation method for sheep carcass images. We first acquire images of the sheep carcass and use augmentation technology to expand the image data, after normalization, using LabelMe to annotate the image and build the sheep carcass image dataset. After that, we establish the ICNet model and train it with transfer learning. The segmentation accuracy, MIoU, and the average processing time of single image are then obtained and used as the evaluation standard of the segmentation effect. In addition, we verify the generalization ability of the ICNet for the sheep carcass image dataset by setting different brightness image segmentation experiments. Finally, the U-Net, DeepLabv3, PSPNet, and Fast-SCNN are introduced for comparative experiments to further verify the segmentation performance of the ICNet. The experimental results show that for the sheep carcass image datasets, the segmentation accuracy and MIoU of our method are 97.68% and 88.47%, respectively. The single image processing time is 83 ms. Besides, the MIoU of U-Net and DeepLabv3 is 0.22% and 0.03% higher than the ICNet, but the processing time of a single image is longer by 186 ms and 430 ms. Besides, compared with the PSPNet and Fast-SCNN, the MIoU of the ICNet model is increased by 1.25% and 4.49%, respectively. However, the processing time of a single image is shorter by 469 ms and expands by 7 ms, respectively.

scholarly journals Lane Detection Algorithm for Intelligent Vehicles in Complex Road Conditions and Dynamic Environments

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3166 ◽  
Author(s):  
Cao ◽  
Song ◽  
Song ◽  
Xiao ◽  
Peng
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Processing Time ◽  
Dynamic Environments ◽  
Detection Algorithm ◽  
Intelligent Vehicles ◽  
Lane Detection ◽  
Detection Accuracy ◽  
Traditional Methods ◽  
Time Performance

Lane detection is an important foundation in the development of intelligent vehicles. To address problems such as low detection accuracy of traditional methods and poor real-time performance of deep learning-based methodologies, a lane detection algorithm for intelligent vehicles in complex road conditions and dynamic environments was proposed. Firstly, converting the distorted image and using the superposition threshold algorithm for edge detection, an aerial view of the lane was obtained via region of interest extraction and inverse perspective transformation. Secondly, the random sample consensus algorithm was adopted to fit the curves of lane lines based on the third-order B-spline curve model, and fitting evaluation and curvature radius calculation were then carried out on the curve. Lastly, by using the road driving video under complex road conditions and the Tusimple dataset, simulation test experiments for lane detection algorithm were performed. The experimental results show that the average detection accuracy based on road driving video reached 98.49%, and the average processing time reached 21.5 ms. The average detection accuracy based on the Tusimple dataset reached 98.42%, and the average processing time reached 22.2 ms. Compared with traditional methods and deep learning-based methodologies, this lane detection algorithm had excellent accuracy and real-time performance, a high detection efficiency and a strong anti-interference ability. The accurate recognition rate and average processing time were significantly improved. The proposed algorithm is crucial in promoting the technological level of intelligent vehicle driving assistance and conducive to the further improvement of the driving safety of intelligent vehicles.

TIME-DOMAIN INTERPOLATION ON GRAPHICS PROCESSING UNIT

2011 ◽  
Vol 04 (01) ◽  
pp. 89-95 ◽  
Author(s):  
XIQI LI ◽  
GUOHUA SHI ◽  
YUDONG ZHANG
Keyword(s):  
Signal Processing ◽  
Real Time ◽  
Time Domain ◽  
Processing Time ◽  
Graphics Processing Unit ◽  
Interpolation Method ◽  
Time Signal ◽  
Processing Unit ◽  
Graphics Processing ◽  
Sd Oct

The signal processing speed of spectral domain optical coherence tomography (SD-OCT) has become a bottleneck in a lot of medical applications. Recently, a time-domain interpolation method was proposed. This method can get better signal-to-noise ratio (SNR) but much-reduced signal processing time in SD-OCT data processing as compared with the commonly used zero-padding interpolation method. Additionally, the resampled data can be obtained by a few data and coefficients in the cutoff window. Thus, a lot of interpolations can be performed simultaneously. So, this interpolation method is suitable for parallel computing. By using graphics processing unit (GPU) and the compute unified device architecture (CUDA) program model, time-domain interpolation can be accelerated significantly. The computing capability can be achieved more than 250,000 A-lines, 200,000 A-lines, and 160,000 A-lines in a second for 2,048 pixel OCT when the cutoff length is L = 11, L = 21, and L = 31, respectively. A frame SD-OCT data (400A-lines × 2,048 pixel per line) is acquired and processed on GPU in real time. The results show that signal processing time of SD-OCT can be finished in 6.223 ms when the cutoff length L = 21, which is much faster than that on central processing unit (CPU). Real-time signal processing of acquired data can be realized.

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