Incredible progress has been made in human action recognition(HAR),significantly impacting computer vision applications in sports analytics.However,identifying dynamic and complex movements in sports like badminton re...Incredible progress has been made in human action recognition(HAR),significantly impacting computer vision applications in sports analytics.However,identifying dynamic and complex movements in sports like badminton remains challenging due to the need for precise recognition accuracy and better management of complex motion patterns.Deep learning techniques like convolutional neural networks(CNNs),long short-term memory(LSTM),and graph convolutional networks(GCNs)improve recognition in large datasets,while the traditional machine learning methods like SVM(support vector machines),RF(random forest),and LR(logistic regression),combined with handcrafted features and ensemble approaches,perform well but struggle with the complexity of fast-paced sports like badminton.We proposed an ensemble learning model combining support vector machines(SVM),logistic regression(LR),random forest(RF),and adaptive boosting(AdaBoost)for badminton action recognition.The data in this study consist of video recordings of badminton stroke techniques,which have been extracted into spatiotemporal data.The three-dimensional distance between each skeleton point and the right hip represents the spatial features.The temporal features are the results of Fast Dynamic Time Warping(FDTW)calculations applied to 15 frames of each video sequence.The weighted ensemble model employs soft voting classifiers from SVM,LR,RF,and AdaBoost to enhance the accuracy of badminton action recognition.The E2 ensemble model,which combines SVM,LR,and AdaBoost,achieves the highest accuracy of 95.38%.展开更多
There are a few issues related to the existing symmetric encryption models for color image data,such as the key generation and distribution problems.In this paper,we propose a compression-encryption model to solve the...There are a few issues related to the existing symmetric encryption models for color image data,such as the key generation and distribution problems.In this paper,we propose a compression-encryption model to solve these problems.This model consists of three processes.The first process is the dynamic symmetric key generation.The second one is the compression process,which is followed by encryption using keystreams and S-Boxes that are generated using a chaotic logistic map.The last process is the symmetric key distribution.The symmetric key is encrypted twice using Rivest-Shamir-Adleman(RSA)to provide both authentication and confidentiality.Then,it is inserted into the cipher image using the End of File(EoF)method.The evaluation shows that the symmetric key generator model can produce a random and dynamic symmetric key.Hence,the image data is safe from ciphertext-only attacks.This model is fast and able to withstand entropy attacks,statistical attacks,differential attacks,and brute-force attacks.展开更多
The pinning characteristics of a single crystal NdBaaCu3Oy superconductor at low （40 K）, intermediate （77.3 K） and high （88 K） temperatures were investigated. The experimental results of the critical current den...The pinning characteristics of a single crystal NdBaaCu3Oy superconductor at low （40 K）, intermediate （77.3 K） and high （88 K） temperatures were investigated. The experimental results of the critical current density dc and the apparent pinning potential u o which estimated from magnetic relaxation measurements are compared with the theoretical analysis based on the flux creep-flow model, taking the distribution of the flux pinning strength into account. The number of flux lines in the flux bundle （g2）, the most probable value of pinning strength （Am）, distribution width of pinning strength （σ-2） and other pinning parameters such as m, γ,δ are determined so that a good fit is obtained between the experimental and theoretical results. The behavior of these parameters is discussed in correspondence to the pinning characteristics of low, intermediate and high temperatures. The observed results are approximately consistent with the theoretical predictions of Brandt et al. model of the order-disorder transition.展开更多
Manuscript preprocessing is the earliest stage in transliteration process of manuscripts in Javanese scripts. Manuscript preprocessing stage is aimed to produce images of letters which form the manuscripts to be proce...Manuscript preprocessing is the earliest stage in transliteration process of manuscripts in Javanese scripts. Manuscript preprocessing stage is aimed to produce images of letters which form the manuscripts to be processed further in manuscript transliteration system. There are four main steps in manuscript preprocessing, which are manuscript binarization, noise reduction, line segmentation, and character segmentation for every line image produced by line segmentation. The result of the test on parts of PB.A57 manuscript which contains 291 character images, with 95% level of confidence concluded that the success percentage of preprocessing in producing Javanese character images ranged 85.9% - 94.82%.展开更多
Prosody in speech synthesis systems (text-to-speech) is a determinant of tone, duration, and loudness of speech sound. Intonation is a part of prosody which determines the speech tone. In Indonesian, intonation is det...Prosody in speech synthesis systems (text-to-speech) is a determinant of tone, duration, and loudness of speech sound. Intonation is a part of prosody which determines the speech tone. In Indonesian, intonation is determined by the structure of sentences, types of sentences, and also the position of the word in a sentence. In this study, a model of speech synthesis that focuses on its intonation is proposed. The speech intonation is determined by sentence structure, intonation patterns of the example sentences, and general rules of Indonesian pronunciation. The model receives texts and intonation patterns as inputs. Based on the general principle of Indonesian pronunciation, a prosody file was made. Based on input text, sentence structure is determined and then interval among parts of a sentence (phrase) can be determined. These intervals are used to correct the duration of the initial prosody file. Furthermore, the frequencies in prosody file were corrected using intonation patterns. The final result is prosody file that can be pronounced by speech engine application. Experiment results of studies using the original voice of radio news announcer and the speech synthesis show that the peaks of?F0?are determined by general rules or intonation patterns which are dominant. Similarity test with the PESQ method shows that the result of the synthesis is 1.18 at MOS-LQO scale.展开更多
Machine learning and deep learning are subsets of Artificial Intelligence that have revolutionized object detection and classification in images or videos.This technology plays a crucial role in facilitating the trans...Machine learning and deep learning are subsets of Artificial Intelligence that have revolutionized object detection and classification in images or videos.This technology plays a crucial role in facilitating the transition from conventional to precision agriculture,particularly in the context of weed control.Precision agriculture,which previously relied on manual efforts,has now embraced the use of smart devices for more efficient weed detection.However,several challenges are associated with weed detection,including the visual similarity between weed and crop,occlusion and lighting effects,as well as the need for early-stage weed control.Therefore,this study aimed to provide a comprehensive review of the application of both traditional machine learning and deep learning,as well as the combination of the two methods,for weed detection across different crop fields.The results of this review show the advantages and disadvantages of using machine learning and deep learning.Generally,deep learning produced superior accuracy compared to machine learning under various conditions.Machine learning required the selection of the right combination of features to achieve high accuracy in classifyingweed and crop,particularly under conditions consisting of lighting and early growth effects.Moreover,a precise segmentation stage would be required in cases of occlusion.Machine learning had the advantage of achieving real-time processing by producing smaller models than deep learning,thereby eliminating the need for additional GPUs.However,the development of GPU technology is currently rapid,so researchers are more often using deep learning for more accurate weed identification.展开更多
The information society depends increasingly on risk assessment and management systems as means to adequately protect its key information assets.The availability of these systems is now vital for the protection and ev...The information society depends increasingly on risk assessment and management systems as means to adequately protect its key information assets.The availability of these systems is now vital for the protection and evolution of companies.However,several factors have led to an increasing need for more accurate risk analysis approaches.These are:the speed at which technologies evolve,their global impact and the growing requirement for companies to collaborate.Risk analysis processes must consequently adapt to these new circumstances and new technological paradigms.The objective of this paper is,therefore,to present the results of an exhaustive analysis of the techniques and methods offered by the scientific community with the aim of identifying their main weaknesses and providing a new risk assessment and management process.This analysis was carried out using the systematic review protocol and found that these proposals do not fully meet these new needs.The paper also presents a summary of MARISMA,the risk analysis and management framework designed by our research group.The basis of our framework is the main existing risk standards and proposals,and it seeks to address the weaknesses found in these proposals.MARISMA is in a process of continuous improvement,as is being applied by customers in several European and American countries.It consists of a risk data management module,a methodology for its systematic application and a tool that automates the process.展开更多
1-inkdot alternating pushdown automaton is a slightly modified alternating pushdown automaton with the additional power of marking at most 1 tape-cell on the input （with an inkdot） once. This paper investigates the ...1-inkdot alternating pushdown automaton is a slightly modified alternating pushdown automaton with the additional power of marking at most 1 tape-cell on the input （with an inkdot） once. This paper investigates the closure property of sublogarithmic space-bounded 1-inkdot alternating pushdown automata with only existential （universal） states, and shows, for example, that for any function L（n） such that L（n） ≥ log logn and L（n） = o（log n）, the class of sets accepted by weakly （strongly） L（n） space-bounded 1-inkdot two-way alternating pushdown automata with only existential （universal） states is not closed under concatenation with regular sets, length-preserving homomorphism, and Kleene closure.展开更多
In this Letter, we propose an elemental image regeneration method of three-dimensional（3D） integral imaging for occluded objects using a plenoptic camera. In conventional occlusion removal techniques, the informatio...In this Letter, we propose an elemental image regeneration method of three-dimensional（3D） integral imaging for occluded objects using a plenoptic camera. In conventional occlusion removal techniques, the information of the occlusion layers may be lost. Thus, elemental images have cracked parts, so the visual quality of the reconstructed 3D image is degraded. However, these cracked parts can be interpolated from adjacent elemental images. Therefore, in this Letter, we try to improve the visual quality of reconstructed 3D images by interpolating and regenerating virtual elemental images with adjacent elemental images after removing the occlusion layers. To prove our proposed method, we carry out optical experiments and calculate performance metrics such as the mean square error（MSE） and the peak signal-to-noise ratio（PSNR）.展开更多
Macronutrient deficiency inhibits the growth and development of chili plants.One of the non-destructive methods that plays a role in processing plant image data based on specific characteristics is computer vision.Thi...Macronutrient deficiency inhibits the growth and development of chili plants.One of the non-destructive methods that plays a role in processing plant image data based on specific characteristics is computer vision.This study uses 5166 image data after augmentation process for six plant health conditions.But the analysis of one feature cannot represent plant health condition.Therefore,a careful combination of features is required.This study combines three types of features with HSV and RGB for color,GLCM and LBP for texture,and Hu moments and centroid distance for shapes.Each feature and its combination are trained and tested using the same MLP architecture.The combination of RGB,GLCM,Hu moments,and Distance of centroid features results the best performance.In addition,this study compares the MLP architecture used with previous studies such as SVM,Random Forest Technique,Naive Bayes,and CNN.CNN produced the best performance,followed by SVM and MLP,with accuracy reaching 97.76%,90.55%and 89.70%,respectively.Although MLP has lower accuracy than CNN,the model for identifying plant health conditions has a reasonably good success rate to be applied in a simple agricultural environment.展开更多
基金supported by the Center for Higher Education Funding(BPPT)and the Indonesia Endowment Fund for Education(LPDP),as acknowledged in decree number 02092/J5.2.3/BPI.06/9/2022。
文摘Incredible progress has been made in human action recognition(HAR),significantly impacting computer vision applications in sports analytics.However,identifying dynamic and complex movements in sports like badminton remains challenging due to the need for precise recognition accuracy and better management of complex motion patterns.Deep learning techniques like convolutional neural networks(CNNs),long short-term memory(LSTM),and graph convolutional networks(GCNs)improve recognition in large datasets,while the traditional machine learning methods like SVM(support vector machines),RF(random forest),and LR(logistic regression),combined with handcrafted features and ensemble approaches,perform well but struggle with the complexity of fast-paced sports like badminton.We proposed an ensemble learning model combining support vector machines(SVM),logistic regression(LR),random forest(RF),and adaptive boosting(AdaBoost)for badminton action recognition.The data in this study consist of video recordings of badminton stroke techniques,which have been extracted into spatiotemporal data.The three-dimensional distance between each skeleton point and the right hip represents the spatial features.The temporal features are the results of Fast Dynamic Time Warping(FDTW)calculations applied to 15 frames of each video sequence.The weighted ensemble model employs soft voting classifiers from SVM,LR,RF,and AdaBoost to enhance the accuracy of badminton action recognition.The E2 ensemble model,which combines SVM,LR,and AdaBoost,achieves the highest accuracy of 95.38%.
文摘There are a few issues related to the existing symmetric encryption models for color image data,such as the key generation and distribution problems.In this paper,we propose a compression-encryption model to solve these problems.This model consists of three processes.The first process is the dynamic symmetric key generation.The second one is the compression process,which is followed by encryption using keystreams and S-Boxes that are generated using a chaotic logistic map.The last process is the symmetric key distribution.The symmetric key is encrypted twice using Rivest-Shamir-Adleman(RSA)to provide both authentication and confidentiality.Then,it is inserted into the cipher image using the End of File(EoF)method.The evaluation shows that the symmetric key generator model can produce a random and dynamic symmetric key.Hence,the image data is safe from ciphertext-only attacks.This model is fast and able to withstand entropy attacks,statistical attacks,differential attacks,and brute-force attacks.
文摘The pinning characteristics of a single crystal NdBaaCu3Oy superconductor at low （40 K）, intermediate （77.3 K） and high （88 K） temperatures were investigated. The experimental results of the critical current density dc and the apparent pinning potential u o which estimated from magnetic relaxation measurements are compared with the theoretical analysis based on the flux creep-flow model, taking the distribution of the flux pinning strength into account. The number of flux lines in the flux bundle （g2）, the most probable value of pinning strength （Am）, distribution width of pinning strength （σ-2） and other pinning parameters such as m, γ,δ are determined so that a good fit is obtained between the experimental and theoretical results. The behavior of these parameters is discussed in correspondence to the pinning characteristics of low, intermediate and high temperatures. The observed results are approximately consistent with the theoretical predictions of Brandt et al. model of the order-disorder transition.
文摘Manuscript preprocessing is the earliest stage in transliteration process of manuscripts in Javanese scripts. Manuscript preprocessing stage is aimed to produce images of letters which form the manuscripts to be processed further in manuscript transliteration system. There are four main steps in manuscript preprocessing, which are manuscript binarization, noise reduction, line segmentation, and character segmentation for every line image produced by line segmentation. The result of the test on parts of PB.A57 manuscript which contains 291 character images, with 95% level of confidence concluded that the success percentage of preprocessing in producing Javanese character images ranged 85.9% - 94.82%.
文摘Prosody in speech synthesis systems (text-to-speech) is a determinant of tone, duration, and loudness of speech sound. Intonation is a part of prosody which determines the speech tone. In Indonesian, intonation is determined by the structure of sentences, types of sentences, and also the position of the word in a sentence. In this study, a model of speech synthesis that focuses on its intonation is proposed. The speech intonation is determined by sentence structure, intonation patterns of the example sentences, and general rules of Indonesian pronunciation. The model receives texts and intonation patterns as inputs. Based on the general principle of Indonesian pronunciation, a prosody file was made. Based on input text, sentence structure is determined and then interval among parts of a sentence (phrase) can be determined. These intervals are used to correct the duration of the initial prosody file. Furthermore, the frequencies in prosody file were corrected using intonation patterns. The final result is prosody file that can be pronounced by speech engine application. Experiment results of studies using the original voice of radio news announcer and the speech synthesis show that the peaks of?F0?are determined by general rules or intonation patterns which are dominant. Similarity test with the PESQ method shows that the result of the synthesis is 1.18 at MOS-LQO scale.
文摘Machine learning and deep learning are subsets of Artificial Intelligence that have revolutionized object detection and classification in images or videos.This technology plays a crucial role in facilitating the transition from conventional to precision agriculture,particularly in the context of weed control.Precision agriculture,which previously relied on manual efforts,has now embraced the use of smart devices for more efficient weed detection.However,several challenges are associated with weed detection,including the visual similarity between weed and crop,occlusion and lighting effects,as well as the need for early-stage weed control.Therefore,this study aimed to provide a comprehensive review of the application of both traditional machine learning and deep learning,as well as the combination of the two methods,for weed detection across different crop fields.The results of this review show the advantages and disadvantages of using machine learning and deep learning.Generally,deep learning produced superior accuracy compared to machine learning under various conditions.Machine learning required the selection of the right combination of features to achieve high accuracy in classifyingweed and crop,particularly under conditions consisting of lighting and early growth effects.Moreover,a precise segmentation stage would be required in cases of occlusion.Machine learning had the advantage of achieving real-time processing by producing smaller models than deep learning,thereby eliminating the need for additional GPUs.However,the development of GPU technology is currently rapid,so researchers are more often using deep learning for more accurate weed identification.
基金the AETHERUCLM(PID2020-112540RB-C42)funded by MCIN/AEI/10.13039/501100011033,SpainALBA-UCLM(TED2021-130355B-C31,id.4809130355-130355-28-521)+1 种基金ALBA-UC(TED2021-130355B-C33,id.3611130630-130630-28-521)funded by the“Ministerio de Ciencia e Innovacion”,Spainsupported by the European Union’s Horizon 2020 Project“CyberSANE”under Grant Agreement No.833683.
文摘The information society depends increasingly on risk assessment and management systems as means to adequately protect its key information assets.The availability of these systems is now vital for the protection and evolution of companies.However,several factors have led to an increasing need for more accurate risk analysis approaches.These are:the speed at which technologies evolve,their global impact and the growing requirement for companies to collaborate.Risk analysis processes must consequently adapt to these new circumstances and new technological paradigms.The objective of this paper is,therefore,to present the results of an exhaustive analysis of the techniques and methods offered by the scientific community with the aim of identifying their main weaknesses and providing a new risk assessment and management process.This analysis was carried out using the systematic review protocol and found that these proposals do not fully meet these new needs.The paper also presents a summary of MARISMA,the risk analysis and management framework designed by our research group.The basis of our framework is the main existing risk standards and proposals,and it seeks to address the weaknesses found in these proposals.MARISMA is in a process of continuous improvement,as is being applied by customers in several European and American countries.It consists of a risk data management module,a methodology for its systematic application and a tool that automates the process.
基金This work is supported by the National Natural Science Foundation of China under Grant No. 60403012,
文摘1-inkdot alternating pushdown automaton is a slightly modified alternating pushdown automaton with the additional power of marking at most 1 tape-cell on the input （with an inkdot） once. This paper investigates the closure property of sublogarithmic space-bounded 1-inkdot alternating pushdown automata with only existential （universal） states, and shows, for example, that for any function L（n） such that L（n） ≥ log logn and L（n） = o（log n）, the class of sets accepted by weakly （strongly） L（n） space-bounded 1-inkdot two-way alternating pushdown automata with only existential （universal） states is not closed under concatenation with regular sets, length-preserving homomorphism, and Kleene closure.
基金supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(No.NRF-2015R1A2A1A16074936)
文摘In this Letter, we propose an elemental image regeneration method of three-dimensional（3D） integral imaging for occluded objects using a plenoptic camera. In conventional occlusion removal techniques, the information of the occlusion layers may be lost. Thus, elemental images have cracked parts, so the visual quality of the reconstructed 3D image is degraded. However, these cracked parts can be interpolated from adjacent elemental images. Therefore, in this Letter, we try to improve the visual quality of reconstructed 3D images by interpolating and regenerating virtual elemental images with adjacent elemental images after removing the occlusion layers. To prove our proposed method, we carry out optical experiments and calculate performance metrics such as the mean square error（MSE） and the peak signal-to-noise ratio（PSNR）.
基金funded by the Directorate of Research and Community Service,Deputy for Strengthening Research and Development,Ministry of Research,Technology/National Research and Innovation Agency of the Republic of Indonesia in the PMDSU program with grant ID 018/E5/PG.02.00.PT/2022 and 1773/UN1/DITLIT/Dit-Lit/PT.01.03/2022.
文摘Macronutrient deficiency inhibits the growth and development of chili plants.One of the non-destructive methods that plays a role in processing plant image data based on specific characteristics is computer vision.This study uses 5166 image data after augmentation process for six plant health conditions.But the analysis of one feature cannot represent plant health condition.Therefore,a careful combination of features is required.This study combines three types of features with HSV and RGB for color,GLCM and LBP for texture,and Hu moments and centroid distance for shapes.Each feature and its combination are trained and tested using the same MLP architecture.The combination of RGB,GLCM,Hu moments,and Distance of centroid features results the best performance.In addition,this study compares the MLP architecture used with previous studies such as SVM,Random Forest Technique,Naive Bayes,and CNN.CNN produced the best performance,followed by SVM and MLP,with accuracy reaching 97.76%,90.55%and 89.70%,respectively.Although MLP has lower accuracy than CNN,the model for identifying plant health conditions has a reasonably good success rate to be applied in a simple agricultural environment.
