Earticle

본고는 조사연속구성의 결합 원리를 규명하기 위해 조사들의 번역 의미와 그들의 문법 기능을 밝혔다. 그리고 그들의 번역 의미 및 그들이 대응되는 중국어 단어의 품사에 따라서 ‘動 詞’, ‘動詞 유래 介詞’, ‘介詞’, ‘副詞’, ‘무대응’, ‘助詞’ 등 6 가지로 분류하며 의미의 실질성에따라 순서를 밝혔다. 이어서 그들이 나타내는 관계의 문법성에 따라 ‘수식 관계’, ‘성분내 관계’, ‘성분간 관계’, ‘자매항 관계’, ‘서술 관계’, ‘순수 관계’ 등 6 가지로 분류하며 문법성에 따라서 순서를 나열하였다. 의미성에 따른 분류ㆍ순서와 문법성에 따른 분류ㆍ순서가 서로 일치하는 방향을 보이므로두 가지 분류ㆍ순서를 합쳐서 더 세분된 분류ㆍ순서를 얻었다. 조사들이 서로 결합해서 조사연속구성으로 형성될 때의 결합 순서가 이 순서와 일치한다. 의미 면에서는 조사의 의미 실질성이 강할수록 그의 결합 위치가 앞으로 가며, 기능 면에서는 조사가 나타내는 관계의 문법성이 강할수록 그 결합 위치가 뒤로 가는 양상을 보인다. 즉 의미성이 강하고 문법성이 약한조사는 명사구와 더 가깝고, 결합 위치가 더 앞에 있으며, 의미성이 약하고 문법성이 강한 조사는 명사구에 더 멀고, 결합 위치가 더 뒤에 있다.

This article analyzed sequential constructions of particle to diagnose the combination principle. According to their translation semantics and the speech of Chinese translation, we divided it into six categories of ‘Verb’, ‘Preposition derived from Verb’, ‘Preposition’, ‘Adverb’, ‘None’, ‘Particle’ and ordered these according to the semantics materiality. Continuously, according to the grammaticality of particle's relation, we divided it into six categories of ‘Relation of modify’, ‘Relation in element’, ‘Relation between elements’, ‘Relation of sister items’, ‘Relation of predicate’, ‘Pure relation’ and ordered these according to the grammaticality. The categories and order made by semantic and grammaticality showed us a same direction. by merging two categories and orders, we got more refined categories and order. Particle's order in sequential constructions is identical with this order. The stronger the particle's semantics materiality is, the more front it's position is. The stronger the grammaticality of particle's relation is, the more rearward it's position is. In a word, the particle with strong semantic and weak grammar is close to noun phrase, the particle with weak semantic and strong grammar is far from noun phrase.

언어는 본래 정보 전달을 목적으로 한다. 따라서 화자의 발화는 청자를 지향하는 언어표출로서 간주관성을 기반으로 하고 있다고 볼 수 있다. 또한 화자가 발화를 구 성할 때 어느 부분을 어느 정도 생략하는가에 따라 간주관성의 지표양상에 차이가 생긴다고 본다. 본고에서는 화자의 발화 형태에 따라 화자의 의도가 달라지며 그 양 상에 따라 화자의 인지작용이 상이하게 활성화되는 점을 탐구하고자 한다. 본 연구에서는 주어에 초점을 맞추고 화자 자신을 나타내는 일인칭대명사와 의미 기능을 부여하는 조사의 공기 유무에 따른 간주관성 지표양상을 분석한다. 주어에 해당하는 ‘일인칭대명사+조사’의 생략이 보이는 발화는 청자가 추측하는 등 인지작 용이 증진되는 발화로 보고 간주관성이 강하게 개입된 것으로 판단된다. 다음으로 ‘일인칭대명사+무조사’의 형태로 주어가 형성된 발화에는 청자가 주의를 환기하는 정도의 인지가 활성화된다. 그리고 주어로서 ‘일인칭대명사+조사’가 명시되는 발화 에는 청자가 추측하는 등의 인지작용 없이 단순히 화자의 발화를 수용하는 것으로 보고 청자의 인지작용이 증진되는 간주관성은 강하게 나타나지 않는 것으로 판단되 었다. 즉 화자의 발화에서 생략이 가장 많이 나타나는 발화일수록 청자의 추측 과정이 필요하게 되고 청자의 인지작용이 활성화되는 것으로 보아 간주관성의 지표 정도가 강하게 나타나는 것으로 고찰된다.

Intersubjectivity is known to differ depending upon the extent of ellipsis used by a speaker when composing an utterance sentence. In the present study, we explored different speaker intentions and found that they depend on the language form of the utterance, and the speaker's cognitive action is activated in different ways. In utterances where “first-person pronoun + particle” was omitted, the listener's cognitive function enhanced, and intersubjectivity was found to be the strongest intervention. Next, in the linguistic form of “first-person pronoun + non-particle”, only the particles were omitted and cognition called attention to the listener works. Since the utterance in which “first-person pronoun + particle” is clearly stated did not show the listener's cognitive effect and received the speaker's utterance unilaterally, there was no intersubjectivity while enhancing the listener's cognitive effect. In short, it was observed that more the utterance omitted in a speaker's utterance, the more the listener's guess is required and the listener's cognitive action is activated, so that the degree of intersubjectivity index becomes stronger.

본 연구는 모발 탈색 시 사용되어지는 탈색제 입자에따른모발손상도를 비교하였으며, 주사전자현미경을 사용하여 세 가지의 탈색제 입자크기를 관찰하였다. 탈색제의 주성분은 과황산암모늄과 과황산칼륨이 주성분인 분말형 탈색제로 각 제품 입자크기는 131μm, 72μm, 48.8μm로 나타났다. 손상도 실험에서 입자가 큰 탈색제보다는 입자가 작은 시술 제품에서 큐티클 층이 일어나거나 박리현상이 거의 일어나지 않았다. 모발의 색상 측정에서 입자 큰 탈색제의 모발시료는 가장 어둡 게 나왔으며, 가장 작은 입자의 탈색 결과가 가장 밝게 나왔다. 연구 결과 모발의 손상도 완화에 더 효과적인 탈색제는 작은 입자를 가진 탈색제를 이용하여 시술하고, 탈색 정도에 따라 입자를 선택하는 것이 탈색력은 높이고 손상도를 낮출 수 있다 고 사료된다. 이는 탈색제 선택에 대해서 우수한 결과를 만들기 위한 방안이며, 그 결과를 토대로 현장에서 탈색 시술시 모발 손상에 대처할 수 있을 것으로 기대 된다.

This study comparatively analyzed hair damage by hair bleach particles and identified three different bleach particle sizes using a scanning electron microscope (SEM). The powdered bleaching agents made of ammonium persulfate (APS) and potassium persulfate had particle sizes of 131μm, 72μm and 48.8μm. According to a hair damage test, cuticle lift-up or peeling hardly occurred when small bleach particles were used. In terms of hair color, the hair bleached with larger bleach particles was the darkest, while the hair bleached with small bleach particles was the brightest. The results found that for bleaching agents that are more effective in easing hair damage, those with little particles should be used. In addition, it appears that the selection of particles depending on the degree of hair bleaching would enhance bleaching effects and reduce hair damage. The above process is a way to get great results in selecting a bleaching agent. It is anticipated that hair damage could be reduced during hair bleaching based on the above results.

이 연구는 한국어 학습자와 한국어 모어 화자의 실제 서사 담화 자료에서 서사 유형에 따라 담화 구조와 조사, 시제의 문법 항목의 사용 양상을 살펴보고자 하였다. 분석 결과, 한국어 학습자는 모어 화자처럼담화 유형과 정보 구조, 그리고 화자의 의도에 따라 과거형과 비과거형의 시제와 {가}, {는}의 조사를 확연히 구분하여 사용하지 못하였다. 이러한 결과는 한국어 교육 현장에서 기존의 문장 층위의 문법 교육에서 보다 확장된 담화 차원의 문법 교육의 필요성을 보여준다. 한국어 모어 화자가 담화 유형과 담화 구조에 따라 시제와 조사를 선택하여 사용하는 것처럼 한국어 학습자 또한 이러한 수준에 도달할 수있도록 문법 교육 내용을 담화 유형과 담화 구조와 연관하여 제공해야한다.

This study focuses on narrative discourses and discourse grammars of Korean language learners. To consider Korean learners' interlanguage, this study aims to compare native Korean speakers with intermediate-level Korean learners. The data was collected from Korean courses at two universities 2011-2012 and was analyzed how they construct narrative discourse structures based on information structures and apply particles, tenses in personal narratives, personalized narratives and impersonal narratives. First, both groups mostly used foreground information in impersonal narrative and it means that narrative discourse structures can be considered universal properties of impersonal narrative genre. But they showed differences in other genres. Second, they mostly used old informations and consequently this means that new informations, which was already mentioned or was inferred, are changed by old informations. Third, this article shows that the native Korean speakers tended to use non-past tenses, ‘eun/neun(은/는)’ particles more frequently than Korean learners and it means that the native Koreans alternate between tenses and particles more often than Korean learners. In conclusion, this study illustrates the interlanguage discourse pattern of the Korean learners. In other words, the Korean learners could not change tenses and particles depending on their intentions and narrative types.

본 연구는 장이론에서 나타나는 이론적인 특징들을 바탕으로, 현대 건축에서 재현되고 있는 물질과 장이 지니고 있는 운동의 결과가 장소성 구축에 어떠한 영향을 주고 있는지를 서술한다. 그리고 이러한 구축의 과정을 통해서 얻어진 이론적 결과를 바탕으로 장이론이 현대건축에서 장소성을 구축함에 있어 하나의 방법론으로 가능성을 지니고 있음을 밝히는 것을 목적으로 한다. 이를 위해서 장이론에 대한 이론적 고찰을 통해서 도출된 상호침투, 중첩, 영역의 특징을 서술하고, 이러한 특징들을 장소성 구축을 위한 도구로 적용하기 위하여, 각각 ‘변환’, ‘융합’, ‘퍼짐’ 등으로 치환한다. 마지막으로 이러한 틀(frame)을 바탕으로 F. Gehry의 작품을 선택하여, 장이론이 건축에서 장소성을 구축하는데 있어 해석적인 방법으로 적용 가능함을 제시한다.

This study is based on theoretical characteristics representing Field theory. This paper argues that the materials reproduced in modern construction and the result of movement with field are related to characteristics of placeness. Based on the result of the process of construction, it aims to show that the Field Theory has possibilities as one of methodologies to construct placeness. It deals with the traits of interpenetration, reiteration and area in relation to theoretical consideration in the Field Theory. Finally, it is substituted to transformation, fusion and spread, in which Frank Gehry's works are described and analyzed from the construction of placeness.

최근 주목을 받고 있는 Particle Filtering은 실제 객체 추적에서 발생하는 비선형, 비 가우시안 분포를 가지는 상태 벡터의 사후확률 을 추정하기 위한 Monte Carlo 시뮬레이션에 기반을 둔 추적 방법론이다. 우리는 본 논문에서 Particle Filtering을 이용한 객체 추적 성능을 향상시킬 수 있는 두 가지 방법론을 제안한다. 첫 번째는 확률이 가장 낮은 샘플을 이전 프레임의 추정된 상태 벡터로 대치하 는 피드백 방법론이고, 두 번째는 객체 확률 분포를 추정된 객체 후보영역에 역투영하여 신뢰구간을 구함으로써 추적 박스의 정확도 를 향상시키는 방법이다. 또한, 실험을 통해 구한 추적 샘플의 진화 방정식을 제시하였다. 우리는 다양한 상황이 설정된 실험 데이터 셋을 구성하여 실험을 실시하여 제안한 방법론의 우수성을 입증하였다.

The object tracking method using particle filtering has been proved successful since it is based on the Monte Carlo simulation to estimate the posterior distribution of the state vector that is nonlinear and non-Gaussian in the real-world situation. In this paper, we present two nobel methods that can improve the performance of the object tracking algorithm based on the particle filtering. First one is the feedback method that replace the low-weighted tracking sample by the estimated state vector in the previous frame. The second one is an tracking box correction method to find an confidence interval of back projection probability on the estimated candidate object area. An sample propagation equation is also presented, which is obtained by experiments. We designed well-organized test data set which reflects various challenging circumstances, and, by using it, experimental results proved that the proposed methods improves the traditional particle filter based object tracking method.

건설사업 초기단계에서 제한된 사업정보를 가지고 개략적인 공사비를 예측하는 것은 사업의 수익성과 다른 사업 추진을 위한 자금의 여유 등을 고려할 때 발주처나 사업주체 같은 건설사업 관계자에게 중요하다. 이에 통계적인 방법과 인공지능기법을 이용하여 사업 초기단계에서의 공사비 예측에 관한 많은 연구가 이루어졌다. 본 논문은 새나 물고기 무리의 군집행동에 영감을 얻은 최적의 해를 찾아가는 진화형 계산기법인 PSO방법을 이용하여 공동주택 건설사업 초기단계에서의 공사비를 예측하는 새로운 방법을 제시한다. NeuroShell Predictor 소프트웨어를 이용하여 219개 공동주택 건설사업자료를 가지고 예측모델을 제안하였다. 소프트웨어에서 제공하는 성능에 대한 통계치와 기존의 연구방법으로 많이 적용된 신경망모델과의 직접적인 비교를 통해서 제안된 PSO기반 예측모델의 성능은 좋은 것으로 판명되었다. 또한, 본 연구에서의 성공적인 적용을 통해서 다른 예측문제의 해결에 있어서 PSO방법의 잠재능력을 보여주었다.

At the initial stage of construction projects, predicting approximate cost with limited information about projects is important to project participants such as owners and developers in that they can estimate expected profits from projects and available funds for other organizational activities. From this point, many researches to predict construction costs at the initial stage have been performed using statistical methods and artificial intelligence methods. This paper proposes a new approach to predict construction costs at the initial stage of apartment housing projects using a PSO(Particle Swarm Optimization) method, a population-based stochastic optimization one inspired by social behaviors of bird flocking or fish schooling. Using the NeuroShell Predictor software, the PSO-based prediction model was suggested with 219 apartment housing projects whose data were obtained from bidding announcements for construction supervisors in the Korea Construction Consulting Engineers Association. From high R-squared and correlation coefficient values provided by the software and in comparison with an artificial neural network(ANN) model, the performance of the suggested prediction model was found to be good. And the results showed that the PSO method can have the potential to provide valuable solutions to other prediction problems.

Orthogonal Frequency Division Multiplexing (OFDM) is the best choice for wireless and wired high data rate communications. The OFDM system has many advantages such as high spectral efficiency, robustness to channel fading, and immunity to impulse interference. However, it has a main drawback, which is the Peak to Average Power Ratio (PAPR). Selecting Mapping (SLM) and Partial Transmit Sequences (PTS) are the two essential techniques for reducing the PAPR; however, they need to send side information to indicate how the transmitter generates signals. In this paper, Particle Swarm Optimization (PSO)-based dummy sub-carriers are inserted with the data to reduce PAPR without transmitting side information. The incorporation of PSO-based Dummy Sub-carriers Insertion (DSI) method on this system is performed by measuring the PAPR at the IFFT output (Inverse Fast Fourier Transform) after adding 6 adaptive sub-carrier sequences to the input data of IFFT. The PSO-based dummy algorithm decides to send the data once the PAPR of this data sequence and dummy sub-carrier is under a specific threshold. Using MATLAB as a mathematical simulation tool, simulation results are presented for a standard OFDM network model. The network model has been simulated in an Additive White Gaussian Noise (AWGN) channel environment to investigate PAPR and Bit-Error Rate (BER) performance. The simulation results show that the dummy sequence based on PSO reduces PAPR down to 4 dB compared to a conventional OFDM system and down to 3 dB compared with the clipping technique for the same BER performance.

Recently new materials and technologies for construction industry have been introduced. So, existing fire safety technology can no longer handle the current situation effectively. Developed countries have already adopted performance-based approaches to structural fire safety. Prior to the introduction of performance-based design, studies many variables have to be conducted. Among these, the heat flow is directly related to fire behavior and affects direction in spread of fire. In addition, heat flow is largely affected by the factors such as location and size of openings. This paper aims to evaluate the applicability of moving particle semi-implicit (MPS) method for heat flow analysis. In this paper, a heat flow analysis program is designed adding the energy equations and buoyancy term in the algorithm that Koshizuka originally developed. In addition a numerical analysis and experiment had been conducted and compared through temperature distributions to verify the reliability of the program. Experiment shows very close result as what is analyzed by the program, confirming the applicability of MPS method for heat flow analysis.

Task scheduling is one of the most important issues in heterogeneous environments when high efficiency is required. Because task scheduling is a Nondeterministic Polynomial (NP)-hard problem, many evolutionary algorithms have been adopted to solve this problem. Since the convergence speed of solutions in population-based algorithms is low, they are integrated with local search algorithms. Thus, in this paper, to optimize the task scheduling makespan, a hybrid particle swarm optimization and hill climbing algorithm is proposed. The experimental results on random and scientific Directed Acyclic Graph (DAG) showed that the proposed algorithm performs effectively in terms of the makespan compared to the current well-known heuristic and particle swarm optimization algorithms.

본 논문에서는 휴먼 행동 인식에 있어 수집 데이터의 특성에 따라 기존 PSO (Particle Swarm Optimization) 알고리즘의 경계선 부분에서 발생되는 문제을 군집들 사이의 거리를 기반으로 개선하는 알고리즘을 제안한다. PSO를 이용한 휴먼 행동 인식 방법은 입력 데이터를 경계값과 비교하여 인식하므로 상으로 빠르고 간단한 방 법이다. 하지만, 착용형 장치와 센서를 이용하는 휴먼 행동 인식에서와 같이 데이터 수집 환경의 변화에 따라 데이 터 특성의 변화가 심하여 학습 데이터와 테스트 데이터 사이의 차이가 생기는 경우 경계값 내에 포함되지 못하는 입 력 데이터들로 인해 성능이 하된다. 이를 해결하기 해 본 논문에서는 경계를 벗어나는 입력 데이터에 해 입력 데이터와 각 군집들의 경계와의 거리를 이용하여 행동 인식 성능을 개선한다. 실험 결과 앉기, 서기, 그리고 걷기와 같은 3 가지 휴먼 행동에 해 SVM (Support Vector Machine) MLP (Multi-Layer Perceptron)과 기존 PSO와 비교하여 최 약 18%의 인식 성능 개선을 가져오는 것을 확인하다.

This paper proposes a modified PSO (Particle Swarm Optimization) algorithm for human activity recognition, which employs distance between boundaries to solve a problem arisen from the boundaries of the existing PSO algorithm. This problem generally caused by inhomogeneous characteristics of the collected human activity data in daily life. Human activity recognition algorithm based on PSO is simple and fast since it classifies input data by comparing boundaries only. However, changes of data collection environment for human activity recognition using wearable and sensor devices cause changes in characteristics of input data, and this results in increase of input data outside of boundaries and decrease of classification performance according to different characteristics between training data and test data. In order to solve this problem, this paper increases performance by correcting boundaries of clusters using the distance between input data and every clusters for input data outside of every boundaries. Experimental results show that the proposed method demonstrates maximum 18% better performance comparing with SVM (Support Vector Machine), MLP (Muti-Layer Perceptron) and the existing PSO respectively for 3 human activities such as sitting, standing, and walking.

An experiment was conducted to evaluate the effect of form and particle size of feed supplemented with L- threonineon growth performance, carcass characteristic and blood biochemical parameters of broiler chickens. The experimentaldesign was a 2 × 2 × 2 factorial arrangement of treatments evaluating two feed forms (pellet or mash), two feed particlesizes (fine or course), and two inclusion rates of dietary L-threonine (with or without) which adopted from 7 to 42 daysof age. In this experiment, 360 a day old chicks in two sexes were assigned in each treatment and each experimentalunit was included 15 chicks. Feed consumption and weight gain were measured weekly. At 35 days of age, bloodsamples were taken to analysis blood biochemical parameters. At the end of the experimental period, two birds wereslaughtered in each treatment and carcass analysis was carried out. The results showed that the effect of feed form onbody weight gain and feed intake in whole of experimental period was significant (P < 0.05). Broilers fed pelleted dietshad more weight gain than the mash group. Growth performance parameters were not affected by feed particle sizeand dietary L-threonine supplementation in whole of experimental period (P > 0.05). The results of carcass analysisshowed that liver and gizzard relative weights were influenced by feed form (P < 0.05). However, pancreas and liverrelative weights were affected by feed particle size and dietary L-threonine supplementation, respectively (P < 0.05). Triglyceride and VLDL levels were affected by feed form and dietary L-threonine supplementation (P < 0.05). The effectof feed particle size on blood biochemical parameters was not significant (P > 0.05). In conclusion, the experimentalresults indicated that feed form increased feed consumption and weight gain in whole of experimental period(1 to 42 days of age) while feed particle size and dietary L-threonine had no effect on broiler performance.

The different issues in the wireless channel, such as multipath fading, shadowing, and hidden terminal problems, limit the sensing abilities of the single user in a cognitive radio network (CRN). In this connection, it is more suitable to follow cooperative spectrum sensing (CSS). However, the various categories of malicious users (MUs) pose threats to the performance of CSS. These MUs are eager to report false sensing data to the fusion center (FC) to deceive its decision. This work considered the CSS performance in the presence of always yes MU (AYMU), opposite MU (OMU), and always no MU (ANMU). Moreover, the considered particle swarm optimization (PSO) algorithm takes out reports of the MUs using one to many sensing distances (OTMSD) and z-score outlier detection techniques before the final decision. Simulation results validate the effectiveness of the proposed scheme in the presence of MUs.

This study was conducted as a preliminary study for the recycling of palladium and palladium oxide. In this study, thermodynamic equations for the formation of palladium oxide (PdO) are established. Palladium chloride is dissolved into hydrochloric acid to generate a palladium chloride solution. Nanosized palladium oxide powder with an average particle size below 30?nm were generated from this raw material solution by means of a spray pyrolysis process. The palladium oxide particles were composed of a single solid crystal. The results of XRD analysis showed that only a PdO phase of the generated powder was formed. And, the specific surface area of the generated palladium powder was approximately 32 m 2 /g.

Machine learning-based sentiment classification is the best-performing method to understand public sentiment. However, the method has some problems, such as noisy features and high-dimensional feature space which affect the sentiment classification performance. To address the problems, this paper proposes a new feature selection using hybrid Query Expansion Ranking and Binary Particle Swarm Optimization with Adaptive Inertia Weights. The proposed method was validated using five tweet datasets on different topics both in Indonesian and English, and compared with state-of-the-art of filter and wrapper-based feature selection methods. Experimental results show the proposed method significantly improves sentiment classification performance and decrease computational time.

Orthogonal frequency division multiplexing (OFDM) is a multicarrier modulation technique for high-speed data transmission in wireless communications. In an OFDM system, a large number of subcarriers are used to transmit the modulated symbols and consequently, the OFDM signals have a high peak-to-average power ratio (PAPR). To reduce the high PAPR, we propose a partial transmit sequence (PTS) method based on the adaptive particle swarm optimization. Also, the proposed method efficiently search the optimal combination of phase rotation factors to decrease the computational complexity. Experimental results show that the proposed method significantly has reduced the PAPR and computational complexity.

In this study, waste ITO target is dissolved into hydrochloric acid to generate a complex indium-tin chloride solution. Nano sized ITO powder with an average particle size below 30 nm are generated from these raw material solutions by spray pyrolysis process. Also, in this study, thermodynamic equations for the formation of indium-tin oxide (ITO) are established. As the reaction temperature increased from 800 o C to 900 o C, the proportion and size of the spherical droplet shape in which nano sized particles aggregated gradually decreased, and the surface structure gradually became densified. When the reaction temperature was 800 o C, the average particle size of the generated powder was about 20 nm, and no significant sintering was observed. At a reaction temperature of 900 o C, the split of the droplet was more severe than at 800 o C, and the rate of maintenance of the initial atomized droplet shape decreased sharply. The average particle size of the powder formed was about 25 nm. The ITO particles were com-posed of single solid crystals, regardless of reaction temperature. XRD analysis showed that only the ITO phase was formed. Remarkably, the specific surface area decreased by about 30% as the reaction temperature increased from 800°C to 900°C.

When the ambient air pressure was 0.1 kg/cm , there were few spherically formed droplets, which showed very badly fragmented state. The average particle size of the particles constituting the droplet was about 40 nm. When the air pressure increased to 0.5 kg/cm 2 , the ratio of the spherical droplet forms increased, but still showed a state of severe disruption. The average particle size of the particles was reduced to about 35 nm. As the air pressure increased to 3 kg/cm 2 , the ratio of spherical droplet form significantly increased, the degree of fragmentation even further decreased and the average particle size decreased to 30 nm. When the air pressure increased from 0.1 to 1 kg/cm 2 , the XRD peak intensity showed little change, but the specific surface area was decreased. As the air pressure increased to 3 kg/cm 2 , the intensity of XRD peaks showed a little decrease, while the specific surface area increased.