Standard bacterial suspensions play a crucial role in microbiological diagnosis.Traditional prepar-ation methods,which rely heavily on manual operations,face challenges such as poor reproducibility,low ef-ficiency,and...Standard bacterial suspensions play a crucial role in microbiological diagnosis.Traditional prepar-ation methods,which rely heavily on manual operations,face challenges such as poor reproducibility,low ef-ficiency,and biosafety concerns.In this study,we propose a high-precision automated colony extraction and separation system that combines large-field imaging and artificial intelligence(AI)to facilitate intelligent screening and localization of colonies.Firstly,a large-field imaging system was developed to capture high-resolution images of 90 mm Petri dishes,achieving a physical resolution of 13.2μm and an imaging speed of 13 frames per second.Subsequently,AI technology was employed for the automatic recognition and localiza-tion of colonies,enabling the selection of target colonies with diameters ranging from 1.9 to 2.3 mm.Next,a three-axis motion control platform was designed,accompanied by a path planning algorithm for the efficient extraction of colonies.An electronic pipette was employed for accurate colony collection.Additionally,a bacterial suspension concentration measurement module was developed,incorporating a 650 nm laser diode as the light source,achieving a measurement accuracy of 0.01 McFarland concentration(MCF).Finally,the system’s performance was validated through the preparation of an Esckerichia coli(E.coli)suspension.After 17 hours of cultivation,E.coli was extracted four times,achieving the target concentration set by the system.This work is expected to enable rapid and accurate microbial sample preparation,significantly reducing de-tection cycles and alleviating the workload of healthcare personnel.展开更多
High-Entropy Alloys(HEAs)exhibit significant potential across multiple domains due to their unique properties.However,conventional research methodologies face limitations in composition design,property prediction,and ...High-Entropy Alloys(HEAs)exhibit significant potential across multiple domains due to their unique properties.However,conventional research methodologies face limitations in composition design,property prediction,and process optimization,characterized by low efficiency and high costs.The integration of Artificial Intelligence(AI)technologies has provided innovative solutions for HEAs research.This review presented a detailed overview of recent advancements in AI applications for structural modeling and mechanical property prediction of HEAs.Furthermore,it discussed the advantages of big data analytics in facilitating alloy composition design and screening,quality control,and defect prediction,as well as the construction and sharing of specialized material databases.The paper also addressed the existing challenges in current AI-driven HEAs research,including issues related to data quality,model interpretability,and cross-domain knowledge integration.Additionally,it proposed prospects for the synergistic development of AI-enhanced computational materials science and experimental validation systems.展开更多
Journal of Systems Engineering and Electronics (Indexed by Science Citation Index,Engineering Index) With the rapid development of mega-constellations,orbital gaming,on-orbit servicing and the advent of the AI era,spa...Journal of Systems Engineering and Electronics (Indexed by Science Citation Index,Engineering Index) With the rapid development of mega-constellations,orbital gaming,on-orbit servicing and the advent of the AI era,space intelligent sensing and manipulation has become a cutting-edge research hotspot in the space domain,attracting growing attention from scholars worldwide.展开更多
This research conducts a comprehensive experimental study of the entire filling system process at the Weishan Lake Rare Earth Mine(WSLREM)in Shandong Province,encompassing tailings thickening,feeding,slurry preparatio...This research conducts a comprehensive experimental study of the entire filling system process at the Weishan Lake Rare Earth Mine(WSLREM)in Shandong Province,encompassing tailings thickening,feeding,slurry preparation,and pipeline transportation.It proposes a complete and efficient upgrade solution for an intelligent paste filling system.The results show that the F1 flocculant was selected to prepare a flocculant solution with a solution concentration of 0.1%.The unit consumption is set to 25 g·t^(-1),and the flocculation and sedimentation effects are optimal when the mass concentration is 15%,with an underflow concentration of 62%.The selection experiment of cementitious material shows that the effect of using new cementitious material is better than that of traditional 32.5R Portland cement.At the same time,rheological experiments on the filling slurry were carried out,and the filling transportation pressure was studied by combining theoretical calculations with numerical simulations.The research results have guiding significance for the debugging of filling pumps and the selection of a filling pipeline.After the application of industrial transformation,the underflow concentration of the sand silo was 64%–66%,the slurry concentration was 68%–72%,the addition range of the cementing material was 1∶16–1∶4,and the filling capacity was 40–60 m^(3)·h^(-1).The intelligent upgrade and transformation of the filling system have yielded remarkable results,providing significant reference value for the intelligent filling transformation of similar mines.展开更多
Deep reinforcement learning algorithms are revolutionizing intelligent decision-making in air combat,drawing widespread attention and extensive research.However,air combat agents trained with these algorithms face sig...Deep reinforcement learning algorithms are revolutionizing intelligent decision-making in air combat,drawing widespread attention and extensive research.However,air combat agents trained with these algorithms face significant challenges,such as limited decision-making capacities due to adversarial training against relatively fixed and singular expert strategies,and a lack of interpretability and reliability in their decisionmaking processes.To tackle these issues,this paper proposes a self-play training mechanism based on policy switching and opponent selection,allowing air combat agents to refine their capabilities via engaging with previous versions of themselves.Additionally,an explainable decision tree model is developed to clarify the decision logic of these agents.Simulations and results demonstrate that the proposed self-play training approach significantly enhances the decision-making abilities of air combat agents,with late-stage agents showing a 38%improvement over early-stage agents in confrontations with an expert strategy.Moreover,the explainable decision tree model effectively elucidates the decision logic and achieves an 86%win rate against the expert strategy,comparable to the 88%win rate of the air combat agents.展开更多
Embodied intelligent systems integrate perception,control,and decision-making within physical agents,and have become a cornerstone of modern aerospace,autonomous driving,and cooperative robotic applications.When opera...Embodied intelligent systems integrate perception,control,and decision-making within physical agents,and have become a cornerstone of modern aerospace,autonomous driving,and cooperative robotic applications.When operating in uncertain and dynamic environments,such systems must address challenges arising from incomplete sensing,unpredictable maneuvers,communication constraints,disturbances,and evolving network structures.展开更多
美国情报理论研究一直处在世界领先地位,拥有丰富的理论成果。选取美国情报学界顶级刊物《studies in intelligence》杂志为文献源,运用Publish or Perish 2.8软件对其解密文献进行统计,揭示美国情报学研究的内容、关注热点及未来趋势,...美国情报理论研究一直处在世界领先地位,拥有丰富的理论成果。选取美国情报学界顶级刊物《studies in intelligence》杂志为文献源,运用Publish or Perish 2.8软件对其解密文献进行统计,揭示美国情报学研究的内容、关注热点及未来趋势,以期为我国intelligence视角下的情报学研究提供参考。展开更多
In order to rapidly and effectively meet the informative demand from commanding decision-making, it is important to build, maintain and mine the intelligence database. The type, structure and maintenance of military i...In order to rapidly and effectively meet the informative demand from commanding decision-making, it is important to build, maintain and mine the intelligence database. The type, structure and maintenance of military intelligence database are discussed. On this condition, a new data-mining arithmetic based on relation intelligence database is presented according to the preference information and the requirement of time limit given by the commander. Furthermore, a simple calculative example is presented to prove the arithmetic with better maneuverability. Lastly, the problem of how to process the intelligence data mined from the intelligence database is discussed.展开更多
The paper presents the coupling of artificial intelligence-AI and Object-oriented methodology applied for the construction of the model-based decision support system MBDSS.The MBDSS is designed for support the strate...The paper presents the coupling of artificial intelligence-AI and Object-oriented methodology applied for the construction of the model-based decision support system MBDSS.The MBDSS is designed for support the strategic decision making lead to the achievemellt of optimal path towardsmarket economy from the central planning situation in China. To meet user's various requirements,a series of innovations in software development have been carried out, such as system formalization with OBFRAMEs in an object-oriented paradigm for problem solving automation and techniques of modules intelligent cooperation, hybrid system of reasoning, connectionist framework utilization,etc. Integration technology has been highly emphasized and discussed in this article and an outlook to future software engineering is given in the conclusion section.展开更多
Artificial intelligence(AI)technology has been increasingly used in medical field with its rapid developments.Echocardiography is one of the best imaging methods for clinical diagnosis of heart diseases,and combining ...Artificial intelligence(AI)technology has been increasingly used in medical field with its rapid developments.Echocardiography is one of the best imaging methods for clinical diagnosis of heart diseases,and combining with AI could further improve its diagnostic efficiency.Though the applications of AI in echocardiography remained at a relatively early stage,a variety of automated quantitative and analytical techniques were rapidly emerging and initially entered clinical practice.The status of clinical applications of AI in echocardiography were reviewed in this article.展开更多
Characterized as automated access,analysis,management,improvement,identification,and efficiency,SaaS BI has played a significant role recently.This essay presents information about SaaS BI,such as,the generic reason f...Characterized as automated access,analysis,management,improvement,identification,and efficiency,SaaS BI has played a significant role recently.This essay presents information about SaaS BI,such as,the generic reason for prevalent,advantages and limitations of SaaS BI,current market overview,tendency of development of SaaS BI,and applications in business.This paper uses the literature study and descriptive study methods in order to analysis the influence and functions of SaaS BI to the businesses and coping strategies.展开更多
Objective To observe the value of self-supervised deep learning artificial intelligence(AI)noise reduction technology based on the nearest adjacent layer applicated in ultra-low dose CT(ULDCT)for urinary calculi.Metho...Objective To observe the value of self-supervised deep learning artificial intelligence(AI)noise reduction technology based on the nearest adjacent layer applicated in ultra-low dose CT(ULDCT)for urinary calculi.Methods Eighty-eight urinary calculi patients were prospectively enrolled.Low dose CT(LDCT)and ULDCT scanning were performed,and the effective dose(ED)of each scanning protocol were calculated.The patients were then randomly divided into training set(n=75)and test set(n=13),and a self-supervised deep learning AI noise reduction system based on the nearest adjacent layer constructed with ULDCT images in training set was used for reducing noise of ULDCT images in test set.In test set,the quality of ULDCT images before and after AI noise reduction were compared with LDCT images,i.e.Blind/Referenceless Image Spatial Quality Evaluator(BRISQUE)scores,image noise(SD ROI)and signal-to-noise ratio(SNR).Results The tube current,the volume CT dose index and the dose length product of abdominal ULDCT scanning protocol were all lower compared with those of LDCT scanning protocol(all P<0.05),with a decrease of ED for approximately 82.66%.For 13 patients with urinary calculi in test set,BRISQUE score showed that the quality level of ULDCT images before AI noise reduction reached 54.42%level but raised to 95.76%level of LDCT images after AI noise reduction.Both ULDCT images after AI noise reduction and LDCT images had lower SD ROI and higher SNR than ULDCT images before AI noise reduction(all adjusted P<0.05),whereas no significant difference was found between the former two(both adjusted P>0.05).Conclusion Self-supervised learning AI noise reduction technology based on the nearest adjacent layer could effectively reduce noise and improve image quality of urinary calculi ULDCT images,being conducive for clinical application of ULDCT.展开更多
In order to accurately forecast the load of power system and enhance the stability of the power network, a novel unascertained mathematics based recurrent neural network (UMRNN) for power intelligence center (PIC) was...In order to accurately forecast the load of power system and enhance the stability of the power network, a novel unascertained mathematics based recurrent neural network (UMRNN) for power intelligence center (PIC) was created through three steps. First, by combining with the general project uncertain element transmission theory (GPUET), the basic definitions of stochastic, fuzzy, and grey uncertain elements were given based on the principal types of uncertain information. Second, a power dynamic alliance including four sectors: generation sector, transmission sector, distribution sector and customers was established. The key factors were amended according to the four transmission topologies of uncertain elements, thus the new factors entered the power intelligence center as the input elements. Finally, in the intelligence handing background of PIC, by performing uncertain and recursive process to the input values of network, and combining unascertained mathematics, the novel load forecasting model was built. Three different approaches were put forward to forecast an eastern regional power grid load in China. The root mean square error (ERMS) demonstrates that the forecasting accuracy of the proposed model UMRNN is 3% higher than that of BP neural network (BPNN), and 5% higher than that of autoregressive integrated moving average (ARIMA). Besides, an example also shows that the average relative error of the first quarter of 2008 forecasted by UMRNN is only 2.59%, which has high precision.展开更多
Artificial intelligence technology is introduced into the simulation of muzzle flow field to improve its simulation efficiency in this paper.A data-physical fusion driven framework is proposed.First,the known flow fie...Artificial intelligence technology is introduced into the simulation of muzzle flow field to improve its simulation efficiency in this paper.A data-physical fusion driven framework is proposed.First,the known flow field data is used to initialize the model parameters,so that the parameters to be trained are close to the optimal value.Then physical prior knowledge is introduced into the training process so that the prediction results not only meet the known flow field information but also meet the physical conservation laws.Through two examples,it is proved that the model under the fusion driven framework can solve the strongly nonlinear flow field problems,and has stronger generalization and expansion.The proposed model is used to solve a muzzle flow field,and the safety clearance behind the barrel side is divided.It is pointed out that the shape of the safety clearance under different launch speeds is roughly the same,and the pressure disturbance in the area within 9.2 m behind the muzzle section exceeds the safety threshold,which is a dangerous area.Comparison with the CFD results shows that the calculation efficiency of the proposed model is greatly improved under the condition of the same calculation accuracy.The proposed model can quickly and accurately simulate the muzzle flow field under various launch conditions.展开更多
文摘Standard bacterial suspensions play a crucial role in microbiological diagnosis.Traditional prepar-ation methods,which rely heavily on manual operations,face challenges such as poor reproducibility,low ef-ficiency,and biosafety concerns.In this study,we propose a high-precision automated colony extraction and separation system that combines large-field imaging and artificial intelligence(AI)to facilitate intelligent screening and localization of colonies.Firstly,a large-field imaging system was developed to capture high-resolution images of 90 mm Petri dishes,achieving a physical resolution of 13.2μm and an imaging speed of 13 frames per second.Subsequently,AI technology was employed for the automatic recognition and localiza-tion of colonies,enabling the selection of target colonies with diameters ranging from 1.9 to 2.3 mm.Next,a three-axis motion control platform was designed,accompanied by a path planning algorithm for the efficient extraction of colonies.An electronic pipette was employed for accurate colony collection.Additionally,a bacterial suspension concentration measurement module was developed,incorporating a 650 nm laser diode as the light source,achieving a measurement accuracy of 0.01 McFarland concentration(MCF).Finally,the system’s performance was validated through the preparation of an Esckerichia coli(E.coli)suspension.After 17 hours of cultivation,E.coli was extracted four times,achieving the target concentration set by the system.This work is expected to enable rapid and accurate microbial sample preparation,significantly reducing de-tection cycles and alleviating the workload of healthcare personnel.
文摘High-Entropy Alloys(HEAs)exhibit significant potential across multiple domains due to their unique properties.However,conventional research methodologies face limitations in composition design,property prediction,and process optimization,characterized by low efficiency and high costs.The integration of Artificial Intelligence(AI)technologies has provided innovative solutions for HEAs research.This review presented a detailed overview of recent advancements in AI applications for structural modeling and mechanical property prediction of HEAs.Furthermore,it discussed the advantages of big data analytics in facilitating alloy composition design and screening,quality control,and defect prediction,as well as the construction and sharing of specialized material databases.The paper also addressed the existing challenges in current AI-driven HEAs research,including issues related to data quality,model interpretability,and cross-domain knowledge integration.Additionally,it proposed prospects for the synergistic development of AI-enhanced computational materials science and experimental validation systems.
文摘Journal of Systems Engineering and Electronics (Indexed by Science Citation Index,Engineering Index) With the rapid development of mega-constellations,orbital gaming,on-orbit servicing and the advent of the AI era,space intelligent sensing and manipulation has become a cutting-edge research hotspot in the space domain,attracting growing attention from scholars worldwide.
基金Supported by the National Natural Science Foundation of China(52004152)Shandong Provincial Natural Science Foundation(ZR2024ME006,ZR2023QE133,ZR2020QE100)+2 种基金Small and Medium-sized Technology Enterprises in Shandong Province(2022TSGC2077)Shandong College Youth Science and Technology Support Program(2023KJ149)National Key Laboratory Open Project Open Fund(2023-JSKSSYS-06)。
文摘This research conducts a comprehensive experimental study of the entire filling system process at the Weishan Lake Rare Earth Mine(WSLREM)in Shandong Province,encompassing tailings thickening,feeding,slurry preparation,and pipeline transportation.It proposes a complete and efficient upgrade solution for an intelligent paste filling system.The results show that the F1 flocculant was selected to prepare a flocculant solution with a solution concentration of 0.1%.The unit consumption is set to 25 g·t^(-1),and the flocculation and sedimentation effects are optimal when the mass concentration is 15%,with an underflow concentration of 62%.The selection experiment of cementitious material shows that the effect of using new cementitious material is better than that of traditional 32.5R Portland cement.At the same time,rheological experiments on the filling slurry were carried out,and the filling transportation pressure was studied by combining theoretical calculations with numerical simulations.The research results have guiding significance for the debugging of filling pumps and the selection of a filling pipeline.After the application of industrial transformation,the underflow concentration of the sand silo was 64%–66%,the slurry concentration was 68%–72%,the addition range of the cementing material was 1∶16–1∶4,and the filling capacity was 40–60 m^(3)·h^(-1).The intelligent upgrade and transformation of the filling system have yielded remarkable results,providing significant reference value for the intelligent filling transformation of similar mines.
基金supported by the Joint Funds of the National Natural Science Foundation of China(U2341216).
文摘Deep reinforcement learning algorithms are revolutionizing intelligent decision-making in air combat,drawing widespread attention and extensive research.However,air combat agents trained with these algorithms face significant challenges,such as limited decision-making capacities due to adversarial training against relatively fixed and singular expert strategies,and a lack of interpretability and reliability in their decisionmaking processes.To tackle these issues,this paper proposes a self-play training mechanism based on policy switching and opponent selection,allowing air combat agents to refine their capabilities via engaging with previous versions of themselves.Additionally,an explainable decision tree model is developed to clarify the decision logic of these agents.Simulations and results demonstrate that the proposed self-play training approach significantly enhances the decision-making abilities of air combat agents,with late-stage agents showing a 38%improvement over early-stage agents in confrontations with an expert strategy.Moreover,the explainable decision tree model effectively elucidates the decision logic and achieves an 86%win rate against the expert strategy,comparable to the 88%win rate of the air combat agents.
文摘Embodied intelligent systems integrate perception,control,and decision-making within physical agents,and have become a cornerstone of modern aerospace,autonomous driving,and cooperative robotic applications.When operating in uncertain and dynamic environments,such systems must address challenges arising from incomplete sensing,unpredictable maneuvers,communication constraints,disturbances,and evolving network structures.
文摘美国情报理论研究一直处在世界领先地位,拥有丰富的理论成果。选取美国情报学界顶级刊物《studies in intelligence》杂志为文献源,运用Publish or Perish 2.8软件对其解密文献进行统计,揭示美国情报学研究的内容、关注热点及未来趋势,以期为我国intelligence视角下的情报学研究提供参考。
文摘In order to rapidly and effectively meet the informative demand from commanding decision-making, it is important to build, maintain and mine the intelligence database. The type, structure and maintenance of military intelligence database are discussed. On this condition, a new data-mining arithmetic based on relation intelligence database is presented according to the preference information and the requirement of time limit given by the commander. Furthermore, a simple calculative example is presented to prove the arithmetic with better maneuverability. Lastly, the problem of how to process the intelligence data mined from the intelligence database is discussed.
文摘The paper presents the coupling of artificial intelligence-AI and Object-oriented methodology applied for the construction of the model-based decision support system MBDSS.The MBDSS is designed for support the strategic decision making lead to the achievemellt of optimal path towardsmarket economy from the central planning situation in China. To meet user's various requirements,a series of innovations in software development have been carried out, such as system formalization with OBFRAMEs in an object-oriented paradigm for problem solving automation and techniques of modules intelligent cooperation, hybrid system of reasoning, connectionist framework utilization,etc. Integration technology has been highly emphasized and discussed in this article and an outlook to future software engineering is given in the conclusion section.
文摘Artificial intelligence(AI)technology has been increasingly used in medical field with its rapid developments.Echocardiography is one of the best imaging methods for clinical diagnosis of heart diseases,and combining with AI could further improve its diagnostic efficiency.Though the applications of AI in echocardiography remained at a relatively early stage,a variety of automated quantitative and analytical techniques were rapidly emerging and initially entered clinical practice.The status of clinical applications of AI in echocardiography were reviewed in this article.
文摘Characterized as automated access,analysis,management,improvement,identification,and efficiency,SaaS BI has played a significant role recently.This essay presents information about SaaS BI,such as,the generic reason for prevalent,advantages and limitations of SaaS BI,current market overview,tendency of development of SaaS BI,and applications in business.This paper uses the literature study and descriptive study methods in order to analysis the influence and functions of SaaS BI to the businesses and coping strategies.
文摘Objective To observe the value of self-supervised deep learning artificial intelligence(AI)noise reduction technology based on the nearest adjacent layer applicated in ultra-low dose CT(ULDCT)for urinary calculi.Methods Eighty-eight urinary calculi patients were prospectively enrolled.Low dose CT(LDCT)and ULDCT scanning were performed,and the effective dose(ED)of each scanning protocol were calculated.The patients were then randomly divided into training set(n=75)and test set(n=13),and a self-supervised deep learning AI noise reduction system based on the nearest adjacent layer constructed with ULDCT images in training set was used for reducing noise of ULDCT images in test set.In test set,the quality of ULDCT images before and after AI noise reduction were compared with LDCT images,i.e.Blind/Referenceless Image Spatial Quality Evaluator(BRISQUE)scores,image noise(SD ROI)and signal-to-noise ratio(SNR).Results The tube current,the volume CT dose index and the dose length product of abdominal ULDCT scanning protocol were all lower compared with those of LDCT scanning protocol(all P<0.05),with a decrease of ED for approximately 82.66%.For 13 patients with urinary calculi in test set,BRISQUE score showed that the quality level of ULDCT images before AI noise reduction reached 54.42%level but raised to 95.76%level of LDCT images after AI noise reduction.Both ULDCT images after AI noise reduction and LDCT images had lower SD ROI and higher SNR than ULDCT images before AI noise reduction(all adjusted P<0.05),whereas no significant difference was found between the former two(both adjusted P>0.05).Conclusion Self-supervised learning AI noise reduction technology based on the nearest adjacent layer could effectively reduce noise and improve image quality of urinary calculi ULDCT images,being conducive for clinical application of ULDCT.
基金Projects(70572090, 70373017) supported by the National Natural Science Foundation of China
文摘In order to accurately forecast the load of power system and enhance the stability of the power network, a novel unascertained mathematics based recurrent neural network (UMRNN) for power intelligence center (PIC) was created through three steps. First, by combining with the general project uncertain element transmission theory (GPUET), the basic definitions of stochastic, fuzzy, and grey uncertain elements were given based on the principal types of uncertain information. Second, a power dynamic alliance including four sectors: generation sector, transmission sector, distribution sector and customers was established. The key factors were amended according to the four transmission topologies of uncertain elements, thus the new factors entered the power intelligence center as the input elements. Finally, in the intelligence handing background of PIC, by performing uncertain and recursive process to the input values of network, and combining unascertained mathematics, the novel load forecasting model was built. Three different approaches were put forward to forecast an eastern regional power grid load in China. The root mean square error (ERMS) demonstrates that the forecasting accuracy of the proposed model UMRNN is 3% higher than that of BP neural network (BPNN), and 5% higher than that of autoregressive integrated moving average (ARIMA). Besides, an example also shows that the average relative error of the first quarter of 2008 forecasted by UMRNN is only 2.59%, which has high precision.
基金Supported by the Natural Science Foundation of Jiangsu Province of China(Grant No.BK20210347)Supported by the National Natural Science Foundation of China(Grant No.U2141246).
文摘Artificial intelligence technology is introduced into the simulation of muzzle flow field to improve its simulation efficiency in this paper.A data-physical fusion driven framework is proposed.First,the known flow field data is used to initialize the model parameters,so that the parameters to be trained are close to the optimal value.Then physical prior knowledge is introduced into the training process so that the prediction results not only meet the known flow field information but also meet the physical conservation laws.Through two examples,it is proved that the model under the fusion driven framework can solve the strongly nonlinear flow field problems,and has stronger generalization and expansion.The proposed model is used to solve a muzzle flow field,and the safety clearance behind the barrel side is divided.It is pointed out that the shape of the safety clearance under different launch speeds is roughly the same,and the pressure disturbance in the area within 9.2 m behind the muzzle section exceeds the safety threshold,which is a dangerous area.Comparison with the CFD results shows that the calculation efficiency of the proposed model is greatly improved under the condition of the same calculation accuracy.The proposed model can quickly and accurately simulate the muzzle flow field under various launch conditions.
