Auto-encoder-enabled anomaly detection in acceleration data: Use case study in container handling operations

The sudden increase in containerization volumes around the globe has increased the overall number of cargo losses, infrastructure damage, and human errors. Most critical losses occur during handling procedures performed by port cranes while sliding the containers to the inner bays of the ship along the vertical cell guides, damaging the main metal frames and causing the structure to deform and lose its integrity and stability. Strong physical impacts may occur at any given moment, thus in-time information is critical to ensure the clarity of the processes without halting operations. This problem has not been addressed fully in the recent literature, either by researchers of the engineering community or by the logistics companies' representatives. In this paper, we have analyzed the conventional means used to detect these critical impacts and found that they are outdated, having no real-time assessment capability, only post-factum visual evaluation results. More reliable and in-time information could benefit many actors in the transportation chain, making transportation processes more efficient, safer, and reliable. The proposed solution incorporates the monitoring hardware unit and the analytics mechanism, namely the auto-encoder technology, that uses the acceleration parameter to identify sensor data anomalies and informs the end-user if these critical impacts occurred during handling procedures. The proposed auto-encoder analytical method is compared with the impacts detection methodology (IDM), and the result indicates that the proposed solution is well capable of detecting critical events by analyzing the curves of reshaped signals, detecting the same impacts as the IDM, while improving the speed of the short-term detection periods. We managed to detect-predict between 9 and 18 impacts, depending on the axis of container sway. An experimental study suggests that if programmed correctly, the auto-encoder (AE) can be used to detect deviations in time-series events in different container handling scenarios.Web of Science109art. no. 73

Outbound supply chain collaboration modelling based on the automotive industry

The global financial crisis has highlighted shortcomings of logistics operations of many manufacturers. In Europe, the largest share of cargo is transported by road transport, where empty running accounts for about 27%. The statistical result reflects the inefficient use of transportation resources. Today it may be difficult to imagine the whole distribution chain of automobiles manufactured in Europe that would integrate all manufacturers. Nevertheless, in an effort to diminish the shortcomings of transport operations, automotive manufacturers and Logistics Service Providers (LSP) should pay more attention to logistics cooperation. The article presents the specific features of distribution networks of vehicles manufactured in Europe, also providing a scenario of integrating finished vehicle output of different vehicle manufacturers in a single distribution network. The demand for transport resources and efficiency of use of the resources was established according to the scenario. This article is a contribution and a fresh look at the variety of the solutions of transportation problems in modern European automotive industry. First published online: 22 May 201

Application of neural network predictive control methods to solve the shipping container sway control problem in quay cranes

Smart control systems are mostly applied in industry to control the movements of heavy machinery while optimizing overall operational efficiency. Major shipping companies use large quay cranes to load and unload containers from ships and still rely on the experience of on-site operators to perform transportation control procedures using joysticks and visual contact methods. This paper presents the research results of an EU-funded project for the Klaipeda container terminal to develop a novel container transportation security and cargo safety assurance method and system. It was concluded that many risks arise during the container handling procedures performed by the quay cranes and operators. To minimize these risks, the authors proposed controlling the sway of the spreader using a model predictive control method which applies a multi-layer perceptron (MLP) neural network (NN). The paper analyzes current neural network architectures and case studies and provides the engineering community with a unique case study which applies real operation statistical data. Several key training algorithms were tested, and the initial results suggest that the Levenberg-Marquardt (LM) algorithm and variable learning rate backpropagation perform better than methods which use the multi-layer perceptron neural network structure.Web of Science9782657825

Development of the real time situation identification model for adaptive service support in vehicular communication networks domain

The article discusses analyses and assesses the key proposals how to deal with the situation identification for the heterogeneous service support in vehicular cooperation environment. This is one of the most important topics of the pervasive computing. Without the solution it is impossible to adequately respond to the user's needs and to provide needed services in the right place at the right moment and in the right way. In this work we present our developed real time situation identification model for adaptive service support in vehicular communication networks domain. Our solution is different from the others as it uses additional virtual context information source - information from other vehicles which for our knowledge is not addressed in the past. The simulation results show the promising context exchange rate between vehicles. The other vehicles provided additional context source in our developed model helps to increase situations identification level

Priority based tag authentication and routing algorithm for intermodal containers RFID sensor network

Intermodal containers transportation management has always been a serious issue among logistics worldwide companies where the application of secure mobile information technologies (e.g. radio frequency identification systems (RFID) and sensor networks) could significantly improve the current situation by sending managers all the needed transportation conditions information. In this paper, we have focused on improving managerial decision making method by introducing the expert system evaluation functionality in a common software solution CTRMS for additional ICT risks evaluation. The basic risks involved in transportation and the appropriate measures are introduced as well. The pre-defined RFID sensor network was used to develop an optimal tag authentication and routing algorithm where tags and reader authentication protocols were defined and based upon the highest security assurance and the reader to tag response time criterias. A Nearest Neighbor (NN) heuristic approach and a Priority setting method were used to address the problem of routing within the RFID sensor network between tags with the objective function of minimizing the data transfer time between tags with the highest priority values. Computational results also indicate that when the tags have the same level of confidence in the system, they can exchange information without any additional verification, so making the authentication protocol less time consuming and therefore more effective against other proposed protocols

Detection of physical impacts of shipping containers during handling operations using the impact detection methodology

The transportation of cargo inside shipping containers is a risky operation that requires constant monitoring activities and real-time operational actions. Yet, the detection of the real dynamics of the container and the surrounding infrastructure and extraction of true subsequent critical events is still an unresolved issue among engineers. In this paper, we analyze the new physical impact detection method, namely the Impact Detection Methodology (IDM), to detect the most obvious and force-dependent impacts from acceleration data, using the IoT sensor in an experimental environment using the heavy machinery of a seaport. By variating the threshold level, we have observed the changes in the number of impacts detected within three separate case studies. Results suggest that the optimal parameters tend to provide an adequate number of events, yet even the slightest change in the threshold level can increase or decrease the number of detected impacts in a non-linear fashion, making the detection harder, due to unforeseen external impacts on the dataset, the filtering of which is still the main priority of our future research.Web of Science109art. no. 125

Ni-Nb-P-based bulk glass-forming alloys: Superior material properties combined in one alloy family

Ni-Nb-based bulk glass-forming alloys are among the most promising amorphous metals for industrial applications due to their incomparable combination of strength, hardness, elasticity and plasticity. However, the main drawback is the limited glass-forming ability, narrowing the field of application to solely small components. In this study, we show that minor additions of P to the binary Ni-Nb system increase the glass-forming ability by 150 % to a record value of 5 mm. P can be easily added by using an industrial Ni-P pre-alloy which is readily available. The partial substitution of Nb by Ta further boosts the glass-forming ability to values 200 % higher than that of the binary base alloy. Besides conventional X-ray diffraction measurements, the amorphous nature of the samples is verified by high-energy synchrotron X-ray diffraction experiments. Moreover, the mechanical properties of the new alloy compositions are characterized in uniaxial compression tests and Vickers hardness measurements, showing a high engineering yield strength of 3 GPa, an extended plastic regime up to 10 % strain to failure and an increase of the hardness to a maximum value of 1000 HV5. Additionally, calorimetric measurements reveal that the modified alloys feature an extended supercooled liquid region up to 69 K upon heating, permitting thermoplastic micro molding of amorphous feedstock material

Outbound supply chain collaboration modelling based on the automotive industry

The global financial crisis has highlighted shortcomings of logistics operations of many manufacturers. In Europe, the largest share of cargo is transported by road transport, where empty running accounts for about 27%. The statistical result reflects the inefficient use of transportation resources. Today it may be difficult to imagine the whole distribution chain of automobiles manufactured in Europe that would integrate all manufacturers. Nevertheless, in an effort to diminish the shortcomings of transport operations, automotive manufacturers and Logistics Service Providers (LSP) should pay more attention to logistics cooperation. The article presents the specific features of distribution networks of vehicles manufactured in Europe, also providing a scenario of integrating finished vehicle output of different vehicle manufacturers in a single distribution network. The demand for transport resources and efficiency of use of the resources was established according to the scenario. This article is a contribution and a fresh look at the variety of the solutions of transportation problems in modern European automotive industry