Low-light images suffer from low visibility, much noise, uneven illumination distribution, etc. Many existing methods have problems such as over enhancement or insufficient detail enhancement when dealing with low-light images with uneven illumination distribution. To remedy the above shortcomings, we propose a Retinex-based self-supervised low-light image enhancement model (Retinex-SIE), which is mainly composed of three parts: Retinex-based self-supervised image decomposition network (Retinex-DNet), nonlinear conditional illumination enhancement function (NCIEF), and Image Reconstruction (IR). First, a uniform illumination image of the same scene with the low-light image is generated by homomorphic filtering transformation, and the low-light image and the uniform illumination image are input into Retinex-DNet for decomposition to obtain reflectivity, noise and illumination. Then, NCIEF is used to enhance the illumination after decomposition. Finally, the final enhanced image is obtained by multiplying the decomposed reflectance and the enhanced illumination. Experiments on severa challenging low-light image datasets show that Retinex-SIE proposed in this paper can better handle low-light images with uneven illumination distribution and avoid problems such as excessive enhancement or insufficient detail enhancement.
{"title":"Retinex-SIE: self-supervised low-light image enhancement method based on Retinex and homomorphic filtering transformation","authors":"Jiachang Yang, Qin Cheng, Jianming Liu","doi":"10.1117/12.2671157","DOIUrl":"https://doi.org/10.1117/12.2671157","url":null,"abstract":"Low-light images suffer from low visibility, much noise, uneven illumination distribution, etc. Many existing methods have problems such as over enhancement or insufficient detail enhancement when dealing with low-light images with uneven illumination distribution. To remedy the above shortcomings, we propose a Retinex-based self-supervised low-light image enhancement model (Retinex-SIE), which is mainly composed of three parts: Retinex-based self-supervised image decomposition network (Retinex-DNet), nonlinear conditional illumination enhancement function (NCIEF), and Image Reconstruction (IR). First, a uniform illumination image of the same scene with the low-light image is generated by homomorphic filtering transformation, and the low-light image and the uniform illumination image are input into Retinex-DNet for decomposition to obtain reflectivity, noise and illumination. Then, NCIEF is used to enhance the illumination after decomposition. Finally, the final enhanced image is obtained by multiplying the decomposed reflectance and the enhanced illumination. Experiments on severa challenging low-light image datasets show that Retinex-SIE proposed in this paper can better handle low-light images with uneven illumination distribution and avoid problems such as excessive enhancement or insufficient detail enhancement.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122630339","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}
In order to improve the effect of ring network cabinet life prediction, the deep neural network life prediction method based on the characteristics of internal distribution of ring network cabinet is studied. Using the optimal wavelet packet transform method, the local discharge characteristics of ring network cabinet are extracted. Nuclear principal component analysis was used to reduce dimension to deal with the local discharge characteristics of ring network cabinet. The bidirectional long-term memory deep neural network was established. The local distribution characteristics after dimensionality reduction were input into the network and the autoregressive comprehensive moving average model, and the life prediction results of the ring network cabinet with nonlinear and linear characteristics were output. The final life estimation results are obtained by combining the two estimation results. Experimental results show that the algorithm can effectively extract and reduce the dimension of the internal local discharge features of ring network cabinet. It can accurately predict the service life of the ring network cabinet under different types of local distribution. Under different local discharge intensities, the R-square coefficient of the algorithm for predicting the life of the ring network cabinet is higher, which has better prediction effect.
{"title":"Deep neural network prediction method of ring network tank life based on internal discharge characteristics","authors":"Jianbing Pan, Yanwu Yu, Xiaoping Yang, Zhixiang Deng, Yuxiang Hao, Zaide Xu","doi":"10.1117/12.2671172","DOIUrl":"https://doi.org/10.1117/12.2671172","url":null,"abstract":"In order to improve the effect of ring network cabinet life prediction, the deep neural network life prediction method based on the characteristics of internal distribution of ring network cabinet is studied. Using the optimal wavelet packet transform method, the local discharge characteristics of ring network cabinet are extracted. Nuclear principal component analysis was used to reduce dimension to deal with the local discharge characteristics of ring network cabinet. The bidirectional long-term memory deep neural network was established. The local distribution characteristics after dimensionality reduction were input into the network and the autoregressive comprehensive moving average model, and the life prediction results of the ring network cabinet with nonlinear and linear characteristics were output. The final life estimation results are obtained by combining the two estimation results. Experimental results show that the algorithm can effectively extract and reduce the dimension of the internal local discharge features of ring network cabinet. It can accurately predict the service life of the ring network cabinet under different types of local distribution. Under different local discharge intensities, the R-square coefficient of the algorithm for predicting the life of the ring network cabinet is higher, which has better prediction effect.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124042034","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}
Yingyuan Du, Tao Wu, Gaoyuan Yang, Yuwei Yang, Ge Peng
Poisoned by the edible fungus accident occurred frequently in recent years since that there were no effective and quick recognition methods for the wild fungus. To tackle the problem, a wild fungus classification algorithm based on a deep convolutional neural network (CNN) and Residual Network (ResNet), is proposed in this paper. An optimization method is also proposed for network training. In order to verify the effectiveness of the model and optimization method, a wild fungus database, in total of 1280 images, is used in this paper. The experimental results show that the proposed algorithm can effectively complete the classification task of wild mushrooms, and the optimization algorithm proposed in this paper can also effectively improve the classification effect of the algorithm model.
{"title":"Classification algorithm based on convolutional neural network for wild fungus","authors":"Yingyuan Du, Tao Wu, Gaoyuan Yang, Yuwei Yang, Ge Peng","doi":"10.1117/12.2671050","DOIUrl":"https://doi.org/10.1117/12.2671050","url":null,"abstract":"Poisoned by the edible fungus accident occurred frequently in recent years since that there were no effective and quick recognition methods for the wild fungus. To tackle the problem, a wild fungus classification algorithm based on a deep convolutional neural network (CNN) and Residual Network (ResNet), is proposed in this paper. An optimization method is also proposed for network training. In order to verify the effectiveness of the model and optimization method, a wild fungus database, in total of 1280 images, is used in this paper. The experimental results show that the proposed algorithm can effectively complete the classification task of wild mushrooms, and the optimization algorithm proposed in this paper can also effectively improve the classification effect of the algorithm model.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124424750","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}
In recent years, due to the development of Internet technology, more and more people begin to connect their daily life with internet technology. Due to its intelligence, simple operation and daily characteristics, wearable devices have gradually entered the public's vision and been accepted by the public. More and more wearable devices appear with a variety of supporting software. This thesis explores how this software use different middleware for different functions, design the UI interface to make people easy to use and use different ways to protect user privacy. To analyze the advantages and disadvantages of the existing software products of wearable devices and propose changes and improvements for future software development, firstly find out some existing wearable device software, and analyze its data processing, functions, adopted protocols, user privacy management, and other aspects. Secondly, through searching corresponding thesis and questionnaires, finds users' dissatisfaction with existing wearable devices. Finally, based on the feedback of users, the data processing, privacy protection and other functions of the existing wearable device software are summarized, and the corresponding direction for the future improvement of wearable devices is put forward.
{"title":"Software technology analysis based on wearable devices","authors":"DongRui Mao, Yuheng Sui, Sihan Wang","doi":"10.1117/12.2671398","DOIUrl":"https://doi.org/10.1117/12.2671398","url":null,"abstract":"In recent years, due to the development of Internet technology, more and more people begin to connect their daily life with internet technology. Due to its intelligence, simple operation and daily characteristics, wearable devices have gradually entered the public's vision and been accepted by the public. More and more wearable devices appear with a variety of supporting software. This thesis explores how this software use different middleware for different functions, design the UI interface to make people easy to use and use different ways to protect user privacy. To analyze the advantages and disadvantages of the existing software products of wearable devices and propose changes and improvements for future software development, firstly find out some existing wearable device software, and analyze its data processing, functions, adopted protocols, user privacy management, and other aspects. Secondly, through searching corresponding thesis and questionnaires, finds users' dissatisfaction with existing wearable devices. Finally, based on the feedback of users, the data processing, privacy protection and other functions of the existing wearable device software are summarized, and the corresponding direction for the future improvement of wearable devices is put forward.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121255666","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}
The traditional artificial potential field method, distance is the only factor to determine the potential field force. When the UAV enters the obstacle's range of action, it is repelled by its potential field, the obstacle will have a repulsive effect on the UAV and as the distance continues to approach, the UAV is subjected to more and more repulsive force, making the UAV avoidance time is too long and the avoidance path is wasted. This paper proposes an improved artificial potential field method for the UAV forward path and obstacles do not intersect and is still in the variety of action of the repulsive potential field, which solves the problem that when the UAV forward direction does not intersect with the obstacles, the UAV is in the range of action of the repulsive potential field and is not subject to repulsive force, avoiding the waste of obstacle avoidance path. It is demonstrated through simulation analysis that the proposed obstacle avoidance algorithm produces superior results.
{"title":"UAV obstacle avoidance based on improved artificial potential field method","authors":"Y. Fan, Yuan Li, X. Li","doi":"10.1117/12.2671404","DOIUrl":"https://doi.org/10.1117/12.2671404","url":null,"abstract":"The traditional artificial potential field method, distance is the only factor to determine the potential field force. When the UAV enters the obstacle's range of action, it is repelled by its potential field, the obstacle will have a repulsive effect on the UAV and as the distance continues to approach, the UAV is subjected to more and more repulsive force, making the UAV avoidance time is too long and the avoidance path is wasted. This paper proposes an improved artificial potential field method for the UAV forward path and obstacles do not intersect and is still in the variety of action of the repulsive potential field, which solves the problem that when the UAV forward direction does not intersect with the obstacles, the UAV is in the range of action of the repulsive potential field and is not subject to repulsive force, avoiding the waste of obstacle avoidance path. It is demonstrated through simulation analysis that the proposed obstacle avoidance algorithm produces superior results.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"12610 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129020242","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}
With the rapid development of 5G technology, the Intelligent and Connected Vehicle (ICV) technology is also evolving and expanding its application scenarios. In order to achieve lower latency and reduce the network load caused by massive data reflow in ICV, MEC (Mobile Edge Computing) technology is introduced to support ICV communication. While MEC technology brings a good experience to users, more and more attacks against Telematics come along, the most common of which is DDoS attacks, which can bring huge losses to telematics systems. Based on this, this paper proposes a DDoS attack detection method based on SAE neural network. The method uses the stacked Auto-encoder-based model proposed in the paper to detect network traffic in the telematics network, feeds the traffic data into the test model, and determines whether the automotive network system is under DDOS attack based on a threshold value. The DDoS attack is detected using the method proposed in the paper, with high detection rates in the training and test sets and stable models. Better experimental results were also obtained by later changing the number of hidden layers in the SAE network to detect DDoS attacks. Comparing the method in this paper with the SVM and CNN methods, the experimental results show that the DDoS attack detection method based on SAE networks works best.
{"title":"A DDoS attack detection method based on AE network in the internet of vehicles","authors":"Shiwen Shen, Yuqiao Ning, Mingming Yu, Zhen Guo, Shihao Xue, Qingyang Wu","doi":"10.1117/12.2671449","DOIUrl":"https://doi.org/10.1117/12.2671449","url":null,"abstract":"With the rapid development of 5G technology, the Intelligent and Connected Vehicle (ICV) technology is also evolving and expanding its application scenarios. In order to achieve lower latency and reduce the network load caused by massive data reflow in ICV, MEC (Mobile Edge Computing) technology is introduced to support ICV communication. While MEC technology brings a good experience to users, more and more attacks against Telematics come along, the most common of which is DDoS attacks, which can bring huge losses to telematics systems. Based on this, this paper proposes a DDoS attack detection method based on SAE neural network. The method uses the stacked Auto-encoder-based model proposed in the paper to detect network traffic in the telematics network, feeds the traffic data into the test model, and determines whether the automotive network system is under DDOS attack based on a threshold value. The DDoS attack is detected using the method proposed in the paper, with high detection rates in the training and test sets and stable models. Better experimental results were also obtained by later changing the number of hidden layers in the SAE network to detect DDoS attacks. Comparing the method in this paper with the SVM and CNN methods, the experimental results show that the DDoS attack detection method based on SAE networks works best.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116326641","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}
Accurately recognizing hand gestures has great significance in assisting human-computer interaction, enhancing user experience, and developing a human-centered ubiquitous system. Due to the inherent complexity of hand gestures, however, how to capture discriminant features of hand motions and build a gesture recognition model remains crucial. To this end, we herein propose a gesture recognition method based on multi-sensor information fusion. Specifically, we first use the accelerometer and surface electromyography (sEMG) sensor to capture the kinematic and physiological signals of hand motions. Afterward, we utilize the sliding window technique to segment the streaming sensor data and extract various features from each segment to return a feature vector. We then optimize a gesture recognition model with the feature vectors. Finally, comparative experiments are conducted on the collected dataset in terms of different machine learning models, different sensors, as well as different types of features. Results show the joint use of sEMG sensor and accelerometer achieves the average accuracy of 97.88% compared to the 90.38% of using sEMG sensor and 84.03% of using accelerometer among four classifiers, which indicates the effectiveness of multi-sensor fusion. Besides, we quantitatively investigate the impact of null gesture on a gesture recognizer.
{"title":"Hand gesture recognition using multi-sensor information fusion","authors":"Aiguo Wang, Huancheng Liu, Jingyu Yan","doi":"10.1117/12.2671270","DOIUrl":"https://doi.org/10.1117/12.2671270","url":null,"abstract":"Accurately recognizing hand gestures has great significance in assisting human-computer interaction, enhancing user experience, and developing a human-centered ubiquitous system. Due to the inherent complexity of hand gestures, however, how to capture discriminant features of hand motions and build a gesture recognition model remains crucial. To this end, we herein propose a gesture recognition method based on multi-sensor information fusion. Specifically, we first use the accelerometer and surface electromyography (sEMG) sensor to capture the kinematic and physiological signals of hand motions. Afterward, we utilize the sliding window technique to segment the streaming sensor data and extract various features from each segment to return a feature vector. We then optimize a gesture recognition model with the feature vectors. Finally, comparative experiments are conducted on the collected dataset in terms of different machine learning models, different sensors, as well as different types of features. Results show the joint use of sEMG sensor and accelerometer achieves the average accuracy of 97.88% compared to the 90.38% of using sEMG sensor and 84.03% of using accelerometer among four classifiers, which indicates the effectiveness of multi-sensor fusion. Besides, we quantitatively investigate the impact of null gesture on a gesture recognizer.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115547055","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}
With the rapid development of computer vision technology in the field of visual fashion, more and more people pay attention to the research on the "reliance" pattern of drama clothing. At present, in the field of clothing image display, research mainly focuses on clothing image recognition, key point detection, clothing recommendation, retrieval and matching. These studies can provide decision support for the design, production, display, sales and other links of drama costumes and bring a new display experience. However, in the realistic application scenarios of clothing "reliance" images, we still face challenges brought by changes in clothing style, materials, cutting, pattern composition and combination methods, which make the effects in recognition, positioning, recommendation and other applications continuously improved through experiments. The method based on depth learning in this paper focuses on clothing "reliance" pattern recognition, key point detection, clothing retrieval and other tasks.
{"title":"Research on pattern recognition and retrieval of reliance based on deep learning","authors":"Chunxia Zhang, Guoyun Zhang","doi":"10.1117/12.2671521","DOIUrl":"https://doi.org/10.1117/12.2671521","url":null,"abstract":"With the rapid development of computer vision technology in the field of visual fashion, more and more people pay attention to the research on the \"reliance\" pattern of drama clothing. At present, in the field of clothing image display, research mainly focuses on clothing image recognition, key point detection, clothing recommendation, retrieval and matching. These studies can provide decision support for the design, production, display, sales and other links of drama costumes and bring a new display experience. However, in the realistic application scenarios of clothing \"reliance\" images, we still face challenges brought by changes in clothing style, materials, cutting, pattern composition and combination methods, which make the effects in recognition, positioning, recommendation and other applications continuously improved through experiments. The method based on depth learning in this paper focuses on clothing \"reliance\" pattern recognition, key point detection, clothing retrieval and other tasks.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115708168","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}
To solve the problems of insufficient local search capability and easily falling into local optimization in the Aquila Optimizer (AO), an aquila optimizer integrating Gaussian walk and somersault strategy (AO-IGWSS) is proposed. Strengthening the exploitation ability, a Gaussian walk strategy is used instead of Levy flight to generate step size adaptively controlled by iteration numbers. Furthermore, to enhance the capability of local optima avoidance, a somersault strategy is introduced to update individuals. The experimental results on nine benchmark test functions prove that the AO-IGWSS can achieve better results than the original AO algorithm, the differential evolution mutation and tangent flight aquila optimizer (DEtanAO), and four other intelligent optimization algorithms.
{"title":"Aquila optimizer integrating Gaussian walk and somersault strategy","authors":"Qiuxiang Yu, Kuntao Ye","doi":"10.1117/12.2671237","DOIUrl":"https://doi.org/10.1117/12.2671237","url":null,"abstract":"To solve the problems of insufficient local search capability and easily falling into local optimization in the Aquila Optimizer (AO), an aquila optimizer integrating Gaussian walk and somersault strategy (AO-IGWSS) is proposed. Strengthening the exploitation ability, a Gaussian walk strategy is used instead of Levy flight to generate step size adaptively controlled by iteration numbers. Furthermore, to enhance the capability of local optima avoidance, a somersault strategy is introduced to update individuals. The experimental results on nine benchmark test functions prove that the AO-IGWSS can achieve better results than the original AO algorithm, the differential evolution mutation and tangent flight aquila optimizer (DEtanAO), and four other intelligent optimization algorithms.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114345393","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}
This study proposes an innovate nonlinear programming model to optimize the shipping cost under the restriction of Emission Control Area (ECA). The arctic shipping route entitles the most potentially route from Asia to Europe to significantly reduce the shipping cost. Therefore, aiming to optimal the existing shipping cost, the optimized model is provided and finds that the feeder ships have better performance on economic cost whether sailing in ECA accordingly with the carbon tax less than 190 USD/tons. The proposed optimization method can well reduce the shipping cost and get better emission performance when choosing the arctic shipping route. And the results also can improve the shipping company’s revenues and maintain environmental sustainability.
{"title":"Nonlinear programming based liner shipping route optimization under ECA restriction","authors":"Qiulan Wang","doi":"10.1117/12.2671177","DOIUrl":"https://doi.org/10.1117/12.2671177","url":null,"abstract":"This study proposes an innovate nonlinear programming model to optimize the shipping cost under the restriction of Emission Control Area (ECA). The arctic shipping route entitles the most potentially route from Asia to Europe to significantly reduce the shipping cost. Therefore, aiming to optimal the existing shipping cost, the optimized model is provided and finds that the feeder ships have better performance on economic cost whether sailing in ECA accordingly with the carbon tax less than 190 USD/tons. The proposed optimization method can well reduce the shipping cost and get better emission performance when choosing the arctic shipping route. And the results also can improve the shipping company’s revenues and maintain environmental sustainability.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"516 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116223211","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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