Pub Date : 2018-09-01DOI: 10.1109/AI4I.2018.8665717
Jonas Stricker, Benno Koeppl, Andi Buzo, Jérôme Kirscher, L. Maurer, G. Pelz
Any component should optimally serve its application by providing exactly the right quantity of features and performances. This is called application fitness of a component. Application fitness can be assessed by simulating component models in an application model. Varying the component's performances may end up in a pass-or fail-behavior with regard to the application requirements. Characterizing the border between this pass and fails states is extremely helpful in the definition of the component's properties. With a number of component properties, this characterization problem gets complex. In this paper, we propose an approach for the planning of simulative experiments, to efficiently characterize this pass/fail border in n dimensions. Especially, smart sampling helps a lot to keep the simulation effort at bay, even if the pass or fail domain falls into a number of unconnected regions. The proposed approach is evaluated taking into account semiconductor components in an automotive electric power steering application. The smart sampling as proposed shows substantial improvements in the number of simulation runs while maintaining a comparable resolution at the border. 1
{"title":"Efficient Simulative Pass/Fail Characterization Applied to Automotive Power Steering","authors":"Jonas Stricker, Benno Koeppl, Andi Buzo, Jérôme Kirscher, L. Maurer, G. Pelz","doi":"10.1109/AI4I.2018.8665717","DOIUrl":"https://doi.org/10.1109/AI4I.2018.8665717","url":null,"abstract":"Any component should optimally serve its application by providing exactly the right quantity of features and performances. This is called application fitness of a component. Application fitness can be assessed by simulating component models in an application model. Varying the component's performances may end up in a pass-or fail-behavior with regard to the application requirements. Characterizing the border between this pass and fails states is extremely helpful in the definition of the component's properties. With a number of component properties, this characterization problem gets complex. In this paper, we propose an approach for the planning of simulative experiments, to efficiently characterize this pass/fail border in n dimensions. Especially, smart sampling helps a lot to keep the simulation effort at bay, even if the pass or fail domain falls into a number of unconnected regions. The proposed approach is evaluated taking into account semiconductor components in an automotive electric power steering application. The smart sampling as proposed shows substantial improvements in the number of simulation runs while maintaining a comparable resolution at the border. 1","PeriodicalId":133657,"journal":{"name":"2018 First International Conference on Artificial Intelligence for Industries (AI4I)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124139782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/AI4I.2018.8665690
Zi-Cheng Han, Guangzhi Qu, Bo Liu, Anyi Liu, Weihua Cai, Dona Burkard
Multicore platforms are pervasively deployed in many different sectors of industry. Hence, it is appealing to accelerate the execution through adapting the sequential programs to the underlying architecture to efficiently utilize the hardware resources, e.g., the multi-cores. However, the parallelization of legacy sequential programs remains a grand challenge due to the complexity of the program analysis and dynamics of the runtime environment. This paper focuses on parallelization planning in that the best parallelization candidates would be determined after the parallelism discovery in the target large sequential programs. In this endeavor, a genetic algorithm based method is deployed to help find an optimal solution considering different aspects from the task decomposition to solution evaluation while achieving the maximized speedup. We have experimented the proposed approach on industrial real time embedded application to reveal excellent speedup results.
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Pub Date : 2018-09-01DOI: 10.1109/AI4I.2018.8665676
Joseph R. Barr
This briefest whirlwind of a tutorial is aimed to pique your appetite by introducing machine learning techniques and procedures on the R platform, especially using the H2O computational framework.
这个简短的教程旨在通过介绍R平台上的机器学习技术和过程，特别是使用H2O计算框架来激起你的兴趣。
{"title":"Machine Learning, A Tutorial with R","authors":"Joseph R. Barr","doi":"10.1109/AI4I.2018.8665676","DOIUrl":"https://doi.org/10.1109/AI4I.2018.8665676","url":null,"abstract":"This briefest whirlwind of a tutorial is aimed to pique your appetite by introducing machine learning techniques and procedures on the R platform, especially using the H2O computational framework.","PeriodicalId":133657,"journal":{"name":"2018 First International Conference on Artificial Intelligence for Industries (AI4I)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124571446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/AI4I.2018.8665680
A. Demiriz
A significant part of the overall automotive market is derived from the used car trade. Determining correctly the used car market values will certainly help achieving fairer trade in many economies. By using the web listings as a proxy data source, we can create some models for the used car pricing based on the asking prices listed in the web adverts. This type of data acquisition requires a thorough data cleaning process to generate dependable statistical models after all. This paper proposes a survival analysis based approach to study the lifetime of the used car listings that can be found at web sites like Craigslist. Pricing models can be easily built to determine the market values of the used-cars from this type of data. One of the most important assumptions in our approach is to consider the delisting of an advert as a sale event. This is also equivalent to the death in the survival analysis context. Since the collected data have labels in terms of sale or not, we can utilize the predictive models to determine whether a particular car at a certain price will be successfully sold or not.
{"title":"Used Car Pricing and Beyond: A Survival Analysis Framework","authors":"A. Demiriz","doi":"10.1109/AI4I.2018.8665680","DOIUrl":"https://doi.org/10.1109/AI4I.2018.8665680","url":null,"abstract":"A significant part of the overall automotive market is derived from the used car trade. Determining correctly the used car market values will certainly help achieving fairer trade in many economies. By using the web listings as a proxy data source, we can create some models for the used car pricing based on the asking prices listed in the web adverts. This type of data acquisition requires a thorough data cleaning process to generate dependable statistical models after all. This paper proposes a survival analysis based approach to study the lifetime of the used car listings that can be found at web sites like Craigslist. Pricing models can be easily built to determine the market values of the used-cars from this type of data. One of the most important assumptions in our approach is to consider the delisting of an advert as a sale event. This is also equivalent to the death in the survival analysis context. Since the collected data have labels in terms of sale or not, we can utilize the predictive models to determine whether a particular car at a certain price will be successfully sold or not.","PeriodicalId":133657,"journal":{"name":"2018 First International Conference on Artificial Intelligence for Industries (AI4I)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129072317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/ai4i.2018.8665697
{"title":"2018 First International Conference on Artificial Intelligence for Industries AI4I 2018","authors":"","doi":"10.1109/ai4i.2018.8665697","DOIUrl":"https://doi.org/10.1109/ai4i.2018.8665697","url":null,"abstract":"","PeriodicalId":133657,"journal":{"name":"2018 First International Conference on Artificial Intelligence for Industries (AI4I)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125836805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/AI4I.2018.8665685
Vinod Muthusamy, Aleksander Slominski, Vatche Isahagian
The stochastic nature of artificial intelligence (AI) models introduces risk to business applications that use AI models without careful consideration. This paper offers an approach to use AI techniques to gain insights on the usage of the AI models and control how they are deployed to a production application.
{"title":"Towards Enterprise-Ready AI Deployments Minimizing the Risk of Consuming AI Models in Business Applications","authors":"Vinod Muthusamy, Aleksander Slominski, Vatche Isahagian","doi":"10.1109/AI4I.2018.8665685","DOIUrl":"https://doi.org/10.1109/AI4I.2018.8665685","url":null,"abstract":"The stochastic nature of artificial intelligence (AI) models introduces risk to business applications that use AI models without careful consideration. This paper offers an approach to use AI techniques to gain insights on the usage of the AI models and control how they are deployed to a production application.","PeriodicalId":133657,"journal":{"name":"2018 First International Conference on Artificial Intelligence for Industries (AI4I)","volume":"20 74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130564763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/AI4I.2018.8665707
Javad Rouzafzoon, P. Helo
Agent-based simulation provides new opportunities to resolve companies' complex problems. This paper presents an agent-based modeling approach for resolving the vehicle scheduling and fleet optimization problem. The method is implemented on case company data and various key performance indicators are generated to measure the efficiency of the solution.
{"title":"Developing Logistics and Supply Chain Management by Using Agent-Based Simulation","authors":"Javad Rouzafzoon, P. Helo","doi":"10.1109/AI4I.2018.8665707","DOIUrl":"https://doi.org/10.1109/AI4I.2018.8665707","url":null,"abstract":"Agent-based simulation provides new opportunities to resolve companies' complex problems. This paper presents an agent-based modeling approach for resolving the vehicle scheduling and fleet optimization problem. The method is implemented on case company data and various key performance indicators are generated to measure the efficiency of the solution.","PeriodicalId":133657,"journal":{"name":"2018 First International Conference on Artificial Intelligence for Industries (AI4I)","volume":"239 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133804825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/ai4i.2018.8665718
{"title":"Message from the ai4i 2018 Program Co-Chairs","authors":"","doi":"10.1109/ai4i.2018.8665718","DOIUrl":"https://doi.org/10.1109/ai4i.2018.8665718","url":null,"abstract":"","PeriodicalId":133657,"journal":{"name":"2018 First International Conference on Artificial Intelligence for Industries (AI4I)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122294883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/AI4I.2018.8665677
Athanasios I. Kyritsis, G. Willems, Michel Deriaz, D. Konstantas
Postoperative rehabilitation is a vital program that re-establishes joint motion and strengthens the muscles around the joint after an orthopedic surgery. This kind of rehabilitation is led by physiotherapists who assess each situation and prescribe appropriate exercises. Modern smart devices have affected every aspect of human life. Newly developed technologies have disrupted the way various industries operate, including the healthcare one. Extensive research has been carried out on how smartphone inertial sensors can be used for activity recognition. However, there are very few studies on systems that monitor patients and detect different gait patterns in order to assist the work of physiotherapists during the said rehabilitation phase, even outside the time-limited physiotherapy sessions, and therefore literature on this topic is still in its infancy. In this paper, we are presenting a gait recognition system that was developed to detect different gait patterns including walking with crutches with various levels of weight-bearing, walking with different frames, limping and walking normally. The proposed system was trained, tested and validated with data of people who have undergone lower body orthopedic surgery, recorded by Hirslanden Clinique La Colline, an orthopedic clinic in Geneva, Switzerland. A gait detection accuracy of 94.9% was achieved among nine different gait classes, as these were labeled by professional physiotherapists.
术后康复是骨科手术后重建关节运动和加强关节周围肌肉的重要项目。这种康复是由物理治疗师领导的，他们评估每种情况并规定适当的运动。现代智能设备已经影响了人类生活的方方面面。新开发的技术已经颠覆了各种行业的运作方式，包括医疗保健行业。关于如何将智能手机惯性传感器用于活动识别，已经进行了广泛的研究。然而，很少有研究系统监测患者和检测不同的步态模式，以协助物理治疗师在上述康复阶段的工作，甚至在时间限制的物理治疗疗程之外，因此，这一主题的文献仍处于起步阶段。在本文中，我们开发了一种步态识别系统，用于检测不同的步态模式，包括不同负重水平的拐杖行走，不同框架的行走，跛行和正常行走。瑞士日内瓦的一家骨科诊所Hirslanden Clinique La Colline记录了接受过下体整形手术的患者的数据，并对该系统进行了培训、测试和验证。在9个不同的步态类别中，步态检测准确率达到94.9%，因为这些是由专业物理治疗师标记的。
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Pub Date : 2018-09-01DOI: 10.1109/ai4i.2018.8665699
{"title":"2018 First IEEE International Conference on Artificial Intelligence for Industries","authors":"","doi":"10.1109/ai4i.2018.8665699","DOIUrl":"https://doi.org/10.1109/ai4i.2018.8665699","url":null,"abstract":"","PeriodicalId":133657,"journal":{"name":"2018 First International Conference on Artificial Intelligence for Industries (AI4I)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125190929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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