Management of containers and carriers in a supply chain that spreads across different intermodal legs of ocean, land, river, rail and air transport is a challenging task in the shipping industry. During the intermodal phase, the triangulation of containers or carriers is a process that is sought to minimize cost by saving a possible transport leg. In this paper, we discuss an optimal triangulation process of containers carried by trucks in an intermodal transport network. We are addressing a specific triangulation process for the trucks engaged in import drops or export pickups of containers such that they can be effectively reused for the next export pickups or import drops in locations within a neighbourhood. We propose a mathematical model to address this problem in the framework of minimum cost network flows. Further, we introduce a heuristic method using the successive shortest path algorithm for the proposed model. The model is analyzed using data from current shipping networks of one of the major shipping industries for its North America database.
The power system and the natural gas network are becoming increasingly interconnected due to the rising number of gas-fired power plants and the emergence of power-to-gas technology. The coordinated operation of power and gas systems is a promising solution to add flexibility to future energy systems, facilitating renewable integration. The optimal operation of the power system is commonly modeled as a mixed-integer linear problem (MILP). Conversely, the natural gas network optimization is a mixed-integer nonlinear problem (MINLP), due to the highly nonlinear and nonconvex Weymouth equation modeling the gas flow in pipelines. Different linearization and convexification approaches have been investigated in the literature for the gas optimization problem, but few authors have verified the quality of the solution. In this paper, a MILP model for the gas problem is developed using an outer approximation technique, and the feasibility of the solution is assessed. A sensitivity analysis on the number of linearization breakpoints is conducted to show the impact on the solution quality and computational time.
Vajira Thambawita; Steven Alexander Hicks; Jonas L. Isaksen; Mette Haug Stensen; Trine B. Haugen; Jørgen K. Kanters; Sravanthi Parasa; Thomas de Lange; Håvard D. Johansen; Dag Johansen; +3 more
Vajira Thambawita; Steven Alexander Hicks; Jonas L. Isaksen; Mette Haug Stensen; Trine B. Haugen; Jørgen K. Kanters; Sravanthi Parasa; Thomas de Lange; Håvard D. Johansen; Dag Johansen; Hugo Lewi Hammer; Pål Halvorsen; Michael Riegler;
Limited access to medical data is a barrier on developing new and efficient machine learning solutions in medicine such as computer-aided diagnosis, risk assessments, predicting optimal treatments and home-based personal healthcare systems. This paper presents DeepSynthBody: a novel framework that overcomes some of the inherent restrictions and limitations of medical data by using deep generative adversarial networks to produce synthetic data with characteristics similar to the real data, so-called DeepSynth (deep synthetic) data. We show that DeepSynthBody can address two key issues commonly associated with medical data, namely privacy concerns (as a result of data protection rules and regulations) and the high costs of annotations. To demonstrate the full pipeline of applying DeepSynthBody concepts and user-friendly functionalities, we also describe a synthetic medical dataset generated and published using our framework. DeepSynthBody opens a new era of machine learning applications in medicine with a synthetic model of the human body.
At present scenario, globally commonly used transportation system are based on IC Engine based vehicles which affects environment due to emission of greenhouse gases. due to the systematic approach for electric transportation system towards green transportation and reducing the crucial issues of global climate changes are the initiative for saving the environment. In recent years the transport industry has become very popular with Electrical Vehicles (EVs). Due to its recent development which will likely to replace the ordinary IC Engine based vehicle in near future to save the nature against the pollution. In this paper, present EV subsystems and its configurations, components of EV are discussed. This paper focus on reviewing the present scenario of EV, latest development of EV and challenges, opportunities for effective deployment of EVs are highlighted. Also there is scope for effective implementation of smart grid technology in EV, grid to vehicle and vehicle to grid, vehicle to home as well as home to vehicle technology along with renewable power incorporation connectivity to EVs and Grid framework and future study developments are also underlined. The paper is intended to include the latest technology and new solutions for future production of electrical vehicles in order to lead to future research into this area.
OpenStreetMap (OSM) is a popular community-driven mapping platform with voluntary contributions from (amateur) cartographers. However, it is a difficult process for the cartographer to identify the areas where she can best contribute to OSM. Furthermore, the current OSM spatial entities are missing many tags; for example, top three road network tags, Name, Source, and Surface, are available only for the 10% of the total road segments. Our paper aims to improve the quantity and quality of the road network tags by actively pushing the nearest road segments for the cartographer to be mapped. We propose a push-based spatial crowdsourcing method to achieve this objective, and validate it by focusing on road segments in OSM. Specifically, we formally define the batch-based maximum road segment task assignment problem and suggest methods based on heuristics like travel distance and road segment task grouping. Finally, our experimental evaluation verify the applicability of our assignment solutions by comparing the resulting number of assigned tasks. With regard to the number of assigned road segments, our junctions-based and road segment-based heuristic methods, outperform the baseline methods by five and two times, respectively.
Energy flow calculation of IES (integrated energy system) aims to calculate the node voltage and pressure, branch current and mass flow rate, working medium temperature and other state parameters according to known information. In the existing researches on energy flow calculation, only the friction head loss is considered and the influence of local head loss is not involved because of the complexity of the pipe structure. The local head loss occurring at abrupt flow boundary changes, such as variable section pipes, pipe inlet and outlet, and pipe connections is inevitable. The output of units and energy flow distribution in the IES will be affected by energy consumption caused by local head loss, so it is necessary to calculate the energy flow with local head loss in mind. In this paper, a local head loss energy model at pipe connection is first established, then a solution model for the energy flow of the electricity-heat IES with local head loss model is established, and the model can be solved by N-R method. Finally, the effectiveness of the model and the influence of local head loss on the whole system are verified through case comparison. The result shows that energy loss caused by local head loss should be avoided although it's a small amount of energy for the whole system.
This paper presents a novel approaches for optimal simultaneous placement of distributed generations and V2G parking lots based on value-based prices method which is neglected in previous researches. In this regard, the technical issues of the network, such as reducing losses and improving the voltage profile, are considered by locating and determining the optimal capacity of scattered production resources and electric vehicles parking lots by considering value-based prices to encourage investors in the network. In the other words the presented method in this paper calculates the actual value of each DG in the active loss reduction and according to this value, determines DG energy price, which is named value based pricing. Therefore consideration of value-based prices of DGs and V2Gs during their optimal placement and proposed optimal search algorithm (PSA) are the main contribution of the paper. In this way, the interests of the operator and the investor are secured simultaneously. To solve the optimization problem, the two algorithms of bird assembly and genetics are compared. The proposed method has been tested on standard IEEE 33 bus distribution systems. The simulation results show that the proposed method is effective in terms of network benefits and incentive packages to attract investors.
Federated learning (FL) is becoming an increasingly popular machine learning paradigm in application scenarios where sensitive data available at various local sites cannot be shared due to privacy protection regulations. In FL, the sensitive data never leaves the local sites and only model parameters are shared with a global aggregator. Nonetheless, it has recently been shown that, under some circumstances, the private data can be reconstructed from the model parameters, which implies that data leakage can occur in FL. In this paper, we draw attention to another risk associated with FL: Even if federated algorithms are individually privacy-preserving, combining them into pipelines is not necessarily privacy-preserving. We provide a concrete example from genome-wide association studies, where the combination of federated principal component analysis and federated linear regression allows the aggregator to retrieve sensitive patient data by solving an instance of the multidimensional subset sum problem. This supports the increasing awareness in the field that, for FL to be truly privacy-preserving, measures have to be undertaken to protect against data leakage at the aggregator.
Large vehicle trajectory data sets can give detailed insight into traffic and congestion that is useful for routing as well as transportation planning. Making information from such data sets available to more users can enable applications that reduce travel time and fuel consumption. However, extracting such information efficiently requires deep knowledge of the underlying schema and indexing methods. To enable more users to extract information from trajectory data, we have developed an API that removes the need to be familiar with the schema. Furthermore, when giving access to trajectory data, privacy concerns often call for the application of anonymization methods before analysis results are made available. In our demonstration, owners of trajectory data are able to experiment with different levels of anonymization to see how this affects the quality of different types of trajectory analysis services implemented on top of a large trajectory data set.
Electric vehicle seems to go well together with the growing societal trend of becoming more self-supplying with renewable electricity produced in the household. However, aligning household electricity production and electric vehicle charging have received little attention in HCI although both areas have been pursued separately for a number of years. In this paper, we present findings from a qualitative study that explore the potential of aligning electric vehicle charging with times where renewable electricity is being produced in the household. We present an empirical qualitative study of 5 households (19 persons) that own electric vehicles and also produce their own renewable electricity. Our findings, described in five themes, reveal that aligning charging and electricity production can be a challenge and tension exist for aligning consumption such as motivation, roles, mobility patterns, and electricity producing technology. We further discuss our findings and possible directions for future HCI research in the field.